{ "expect_column_values_confidence_for_data_label_to_be_greater_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-03-21 11:49:59 -0500 1 year, 4 months ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_column_values_confidence_for_data_label_to_be_greater_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler" ], "contributors": [ "@taylorfturner" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesConfidenceForDataLabelToBeGreaterThanOrEqualToThreshold", "snake_name": "expect_column_values_confidence_for_data_label_to_be_greater_than_or_equal_to_threshold", "short_description": "Expect the column values to have a DataProfiler confidence threshold greater than or equal to the specified threshold for the data label.", "docstring": "Expect the column values to have a DataProfiler confidence threshold greater than or equal to the specified threshold for the data label.\n\nThis function builds upon the custom column map expectations of Great Expectations. This function asks the question a yes/no question of each row in the user-specified column; namely, does the confidence threshold provided by the DataProfiler model exceed the user-specified threshold.\n\n#### Args:\n- column (str): The column name that you want to check.\n- data_label(str): The data label for which you want to check confidences against the threshold value\n- threshold (float): The value, usually as a decimal (e.g. .32), you want to use to flag low confidence predictions\n\ndf.expect_column_values_confidence_for_data_label_to_be_greater_than_or_equal_to_threshold(\ncolumn,\ndata_label=<>,\nthreshold=float(0<=1)\n)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.prediction_confidence_for_data_label_greater_than_or_equal_to_threshold.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.prediction_confidence_for_data_label_greater_than_or_equal_to_threshold.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to have a DataProfiler confidence threshold greater than or equal to the specified threshold for the data label.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_confidence_for_data_label_to_be_less_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-06-28 15:43:31 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_column_values_confidence_for_data_label_to_be_less_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler" ], "contributors": [ "@taylorfturner", "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesConfidenceForDataLabelToBeLessThanOrEqualToThreshold", "snake_name": "expect_column_values_confidence_for_data_label_to_be_less_than_or_equal_to_threshold", "short_description": "Expect the column values to have a DataProfiler confidence threshold less than or equal to the specified threshold for the data label.", "docstring": "Expect the column values to have a DataProfiler confidence threshold less than or equal to the specified threshold for the data label.\n\nThis function builds upon the custom column map expectations of Great Expectations. This function asks the question a yes/no question of each row in the user-specified column; namely, is the confidence threshold provided by the DataProfiler model upper bounded by the user-specified threshold.\n\n#### Args:\n- column (str): The column name that you want to check.\n- data_label(str): The data label for which you want to check confidences against the threshold value\n- threshold (float): The value, usually as a decimal (e.g. .32), you want to use to flag low confidence predictions\n\ndf.expect_column_values_confidence_for_data_label_to_be_greater_than_or_equal_to_threshold(\ncolumn,\ndata_label=<>,\nthreshold=float(0<=1)\n)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.prediction_confidence_for_data_label_less_than_or_equal_to_threshold.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.prediction_confidence_for_data_label_less_than_or_equal_to_threshold.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to have a DataProfiler confidence threshold less than or equal to the specified threshold for the data label.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_to_be_equal_to_or_greater_than_profile_min": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-06-24 15:27:06 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_column_values_to_be_equal_to_or_greater_than_profile_min.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeEqualToOrGreaterThanProfileMin", "snake_name": "expect_column_values_to_be_equal_to_or_greater_than_profile_min", "short_description": "Expect the column values to be greater than or equal to the minimum value of the respective column within the DataProfiler report.", "docstring": "Expect the column values to be greater than or equal to the minimum value of the respective column within the DataProfiler report.\n\nThis function builds upon the custom column map expectations of Great Expectations. This function asks a yes/no question of each row in the user-specified column;\nnamely, is the value greater than or equal to the minimum value of the respective column within the provided profile report generated from the DataProfiler.\n\n#### Args:\n- column(str): The column that you want to check.\n- profile(dict(str, Any)): The report, which is assumed to contain a column of the same name, previously generated using the DataProfiler.\n\ndf.expect_column_values_to_be_equal_to_or_greater_than_profile_min(\ncolumn,\nprofile\n)\n\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.greater_than_or_equal_to_profile_min.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.greater_than_or_equal_to_profile_min.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to be greater than or equal to the minimum value of the respective column within the DataProfiler report.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_to_be_equal_to_or_less_than_profile_max": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-06-24 15:29:32 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_column_values_to_be_equal_to_or_less_than_profile_max.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeEqualToOrLessThanProfileMax", "snake_name": "expect_column_values_to_be_equal_to_or_less_than_profile_max", "short_description": "Expect the column values to be less than or equal to the maximum value of the respective column within the DataProfiler report.", "docstring": "Expect the column values to be less than or equal to the maximum value of the respective column within the DataProfiler report.\n\nThis function builds upon the custom column map expectations of Great Expectations. This function asks a yes/no question of each row in the user-specified column;\nnamely, is the value less than or equal to the maximum value of the respective column within the provided profile report generated from the DataProfiler.\n\n#### Args:\n- column(str): The column that you want to check.\n- profile(dict(str, Any)): The report, which is assumed to contain a column of the same name, previously generated using the DataProfiler.\n\ndf.expect_column_values_to_be_equal_to_or_less_than_profile_max(\ncolumn,\nprofile\n)\n\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.less_than_or_equal_to_profile_max.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.less_than_or_equal_to_profile_max.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to be less than or equal to the maximum value of the respective column within the DataProfiler report.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_to_be_probabilistically_greater_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-03-21 11:49:59 -0500 1 year, 4 months ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_column_values_to_be_probabilistically_greater_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler" ], "contributors": [ "@taylorfturner" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeProbabilisticallyGreaterThanOrEqualToThreshold", "snake_name": "expect_column_values_to_be_probabilistically_greater_than_or_equal_to_threshold", "short_description": "Expect the column values to be probabilistically greater than or equal to the specified threshold.", "docstring": "Expect the column values to be probabilistically greater than or equal to the specified threshold.\n\nThis function builds upon the custom column map expectations of Great Expectations. This function asks a yes/no question of each row in the user-specified column; namely, does the confidence threshold provided by the DataProfiler model exceed the user-specified threshold.\n\n#### Args:\n- column (str): The column name that you want to check.\n- threshold (float): The value, usually as a decimal (e.g. .32), you want to use to flag low confidence predictions\n\ndf.expect_column_values_to_be_probabilistically_greater_than_or_equal_to_threshold(\ncolumn,\nthreshold=float(0<=1)\n)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.prediction_confidence_greater_than_or_equal_to_threshold.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.prediction_confidence_greater_than_or_equal_to_threshold.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to be probabilistically greater than or equal to the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_profile_numeric_columns_diff_between_exclusive_threshold_range": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-15 13:06:18 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_diff_between_exclusive_threshold_range.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsDiffBetweenExclusiveThresholdRange", "snake_name": "expect_profile_numeric_columns_diff_between_exclusive_threshold_range", "short_description": "Expect a statistic's value for a given column of a DataProfiler difference report to be within the specified threshold, exclusive.", "docstring": "Expect a statistic's value for a given column of a DataProfiler difference report to be within the specified threshold, exclusive.\n\nThis expectation takes the difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nEach numerical column will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing lower and upper bounds.\nIt is expected that a statistics value for a given column is within the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n\n#### limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and dicts as values containing two keys:\n\"lower\" denoting the lower bound for the threshold range, and \"upper\" denoting the upper bound for the threshold range.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_diff_between_threshold_range(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": {\"lower\": 2.0, \"upper\": 10.0},\n},\n- \"*\": {\n- \"*\": {\"lower\": 0, \"upper\": 100},\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_diff_between_threshold_range", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's value for a given column of a DataProfiler difference report to be within the specified threshold, exclusive.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_diff_between_inclusive_threshold_range": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-15 13:06:18 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_diff_between_inclusive_threshold_range.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsDiffBetweenInclusiveThresholdRange", "snake_name": "expect_profile_numeric_columns_diff_between_inclusive_threshold_range", "short_description": "Expect a statistic's value for a given column of a DataProfiler difference report to be within the specified threshold, inclusive.", "docstring": "Expect a statistic's value for a given column of a DataProfiler difference report to be within the specified threshold, inclusive.\n\nThis expectation takes the difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nEach numerical column will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing lower and upper bounds.\nIt is expected that a statistics value for a given column is within the specified threshold, inclusive.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n\n#### limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and dicts as values containing two keys:\n\"lower\" denoting the lower bound for the threshold range, and \"upper\" denoting the upper bound for the threshold range.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_diff_between_threshold_range(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": {\"lower\": 2.0, \"upper\": 10.0},\n},\n- \"*\": {\n- \"*\": {\"lower\": 0, \"upper\": 100},\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_diff_between_inclusive_threshold_range", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's value for a given column of a DataProfiler difference report to be within the specified threshold, inclusive.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_diff_greater_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-18 10:28:24 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_diff_greater_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsDiffGreaterThanOrEqualToThreshold", "snake_name": "expect_profile_numeric_columns_diff_greater_than_or_equal_to_threshold", "short_description": "Expect a statistic's value for a given column of a DataProfiler difference report to be greater than or equal to the specified threshold.", "docstring": "Expect a statistic's value for a given column of a DataProfiler difference report to be greater than or equal to the specified threshold.\n\nThis expectation takes the difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nEach numerical column will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold value.\nIt is expected that a statistics value for a given column is greater than or equal to the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and ints or floats as values\ndenoting the threshold that the statistic delta is expectated to exceed.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_diff_greater_than_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0\n},\n- \"*\": {\n- \"*\": 1\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_diff_greater_than_or_equal_to_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's value for a given column of a DataProfiler difference report to be greater than or equal to the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_profile_numeric_columns_diff_greater_than_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-15 13:06:18 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_diff_greater_than_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsDiffGreaterThanThreshold", "snake_name": "expect_profile_numeric_columns_diff_greater_than_threshold", "short_description": "Expect a statistic's value for a given column of a DataProfiler difference report to be greater than the specified threshold.", "docstring": "Expect a statistic's value for a given column of a DataProfiler difference report to be greater than the specified threshold.\n\nThis expectation takes the difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nEach numerical column will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold value.\nIt is expected that a statistics value for a given column is greater than the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and ints or floats as values\ndenoting the threshold that the statistic delta is expectated to exceed.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_diff_greater_than_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0\n},\n- \"*\": {\n- \"*\": 1\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_diff_greater_than_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's value for a given column of a DataProfiler difference report to be greater than the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_diff_less_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-18 10:28:24 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_diff_less_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsDiffLessThanOrEqualToThreshold", "snake_name": "expect_profile_numeric_columns_diff_less_than_or_equal_to_threshold", "short_description": "Expect a statistic's value for a given column of a DataProfiler difference report to be less than or equal to the specified threshold.", "docstring": "Expect a statistic's value for a given column of a DataProfiler difference report to be less than or equal to the specified threshold.\n\nThis expectation takes the difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nEach numerical column will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold value.\nIt is expected that a statistics value for a given column is less than or equal to the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and ints or floats as values\ndenoting the threshold that the statistic delta is expectated to be below.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_diff_less_than_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0\n},\n- \"*\": {\n- \"*\": 1\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_diff_less_than_or_equal_to_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's value for a given column of a DataProfiler difference report to be less than or equal to the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 7.0 }, "expect_profile_numeric_columns_diff_less_than_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-15 13:06:18 -0400 1 year, 1 month ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_diff_less_than_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsDiffLessThanThreshold", "snake_name": "expect_profile_numeric_columns_diff_less_than_threshold", "short_description": "Expect a statistic's value for a given column of a DataProfiler difference report to be less than the specified threshold.", "docstring": "Expect a statistic's value for a given column of a DataProfiler difference report to be less than the specified threshold.\n\nThis expectation takes the difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nEach numerical column will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold value.\nIt is expected that a statistics value for a given column is less than the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and ints or floats as values\ndenoting the threshold that the statistic delta is expectated to exceed.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_diff_less_than_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0\n},\n- \"*\": {\n- \"*\": 1\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_diff_less_than_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's value for a given column of a DataProfiler difference report to be less than the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_percent_diff_between_exclusive_threshold_range": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-21 14:41:44 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_percent_diff_between_exclusive_threshold_range.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsPercentDiffBetweenExclusiveThresholdRange", "snake_name": "expect_profile_numeric_columns_percent_diff_between_exclusive_threshold_range", "short_description": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be within the specified threshold, exclusive.", "docstring": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be within the specified threshold, exclusive.\n\nThis expectation takes the percent difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nEach numerical column percent delta will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing lower and upper bounds.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nIt is expected that a statistic's percent delta for a given column is within the specified threshold, exclusive.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n\n#### limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and dicts as values containing two keys:\n\"lower\" denoting the lower bound for the threshold range, and \"upper\" denoting the upper bound for the threshold range.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_percent_diff_between_exclusive_threshold_range(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": {\"lower\": 0.5, \"upper\": 1.5}, #Indicating 50% lower bound and 150% upper bound\n},\n- \"*\": {\n- \"*\": {\"lower\": 0.0, \"upper\": 2.0}, #Indicating 0% lower bound and 200% upper bound\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_percent_diff_between_threshold_range", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be within the specified threshold, exclusive.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_percent_diff_between_inclusive_threshold_range": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-27 12:02:02 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_percent_diff_between_inclusive_threshold_range.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsPercentDiffBetweenInclusiveThresholdRange", "snake_name": "expect_profile_numeric_columns_percent_diff_between_inclusive_threshold_range", "short_description": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be within the specified threshold, inclusive.", "docstring": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be within the specified threshold, inclusive.\n\nThis expectation takes the percent difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nEach numerical column percent delta will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing lower and upper bounds.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nIt is expected that a statistic's percent delta for a given column is within the specified threshold, inclusive.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n\n#### limit_check_report_keys (dict): A dict, containing column names as keys and dicts as values that contain statistics as keys and dicts as values containing two keys:\n\"lower\" denoting the lower bound for the threshold range, and \"upper\" denoting the upper bound for the threshold range.\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numeric_columns_percent_diff_between_inclusive_threshold_range(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": {\"lower\": 0.5, \"upper\": 1.5}, #Indicating 50% lower bound and 150% upper bound\n},\n- \"*\": {\n- \"*\": {\"lower\": 0.0, \"upper\": 2.0}, #Indicating 0% lower bound and 200% upper bound\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_percent_diff_between_inclusive_threshold_range", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be within the specified threshold, inclusive.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_percent_diff_greater_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-27 12:02:02 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_percent_diff_greater_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsPercentDiffGreaterThanOrEqualToThreshold", "snake_name": "expect_profile_numeric_columns_percent_diff_greater_than_or_equal_to_threshold", "short_description": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be greater than or equal to the specified threshold.", "docstring": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be greater than or equal to the specified threshold.\n\nThis expectation takes the percent difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nEach numerical column percent delta will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nIt is expected that a statistic's percent delta for a given column is greater than or equal to the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict[str, dict[str, float]]): A dict, containing column names as keys and dicts as values that contain statistics as keys and thresholds as values\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numeric_columns_percent_diff_greater_than_or_equal_to_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0.5, #Indicating the threshold for the 'min' statistic in 'column_one' is 50%\n},\n- \"*\": {\n- \"*\": .25, #Indicating the threshold for every statistic in every column is 25%\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_percent_diff_greater_than_or_equal_to_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be greater than or equal to the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_percent_diff_greater_than_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-25 11:46:19 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_percent_diff_greater_than_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsPercentDiffGreaterThanThreshold", "snake_name": "expect_profile_numeric_columns_percent_diff_greater_than_threshold", "short_description": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be greater than the specified threshold.", "docstring": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be greater than the specified threshold.\n\nThis expectation takes the percent difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nEach numerical column percent delta will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nIt is expected that a statistic's percent delta for a given column is greater than the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict[str, dict[str, float]]): A dict, containing column names as keys and dicts as values that contain statistics as keys and thresholds as values\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numerical_columns_percent_diff_greater_than_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0.5, #Indicating the threshold for the 'min' statistic in 'column_one' is 50%\n},\n- \"*\": {\n- \"*\": .25, #Indicating the threshold for every statistic in every column is 25%\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_percent_diff_greater_than_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be greater than the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_percent_diff_less_than_or_equal_to_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-27 12:02:02 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_percent_diff_less_than_or_equal_to_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsPercentDiffLessThanOrEqualToThreshold", "snake_name": "expect_profile_numeric_columns_percent_diff_less_than_or_equal_to_threshold", "short_description": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be less than or equal to the specified threshold.", "docstring": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be less than or equal to the specified threshold.\n\nThis expectation takes the percent difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nEach numerical column percent delta will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nIt is expected that a statistic's percent delta for a given column is less than or equal to the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict[str, dict[str, float]]): A dict, containing column names as keys and dicts as values that contain statistics as keys and thresholds as values\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numeric_columns_percent_diff_less_than_or_equal_to_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0.5, #Indicating the threshold for the 'min' statistic in 'column_one' is 50%\n},\n- \"*\": {\n- \"*\": .25, #Indicating the threshold for every statistic in every column is 25%\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_percent_diff_less_than_or_equal_to_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be less than or equal to the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_profile_numeric_columns_percent_diff_less_than_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-07-25 11:46:19 -0400 1 year ago", "path": "contrib/capitalone_dataprofiler_expectations/capitalone_dataprofiler_expectations/expectations/expect_profile_numeric_columns_percent_diff_less_than_threshold.py", "package": "capitalone_dataprofiler_expectations", "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "import_module_args": [ "capitalone_dataprofiler_expectations.expectations" ], "sys_path": "../../contrib/capitalone_dataprofiler_expectations", "exp_type": "Contrib ProfileNumericColumnsDiffExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "dataprofiler", "dataassistance" ], "contributors": [ "@stevensecreti" ], "requirements": [ "dataprofiler", "tensorflow", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectProfileNumericColumnsPercentDiffLessThanThreshold", "snake_name": "expect_profile_numeric_columns_percent_diff_less_than_threshold", "short_description": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be less than the specified threshold.", "docstring": "Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be less than the specified threshold.\n\nThis expectation takes the percent difference report between the data it is called on and a DataProfiler profile of the same schema loaded from a provided path.\nEach numerical column percent delta will be checked against a user provided dictionary of columns paired with dictionaries of statistics containing a threshold.\nThis function builds upon the custom ProfileNumericColumnsDiff Expectation of Capital One's DataProfiler Expectations.\nIt is expected that a statistic's percent delta for a given column is less than the specified threshold.\n\n#### Args:\n- profile_path (str): A path to a saved DataProfiler profile object on the local filesystem.\n- limit_check_report_keys (dict[str, dict[str, float]]): A dict, containing column names as keys and dicts as values that contain statistics as keys and thresholds as values\n- mostly (float - optional): a value indicating the lower bound percentage of successful values that must be present to evaluate to success=True.\nvalidator.expect_profile_numeric_columns_percent_diff_less_than_threshold(\n- profile_path = \"C:/path_to/my_profile.pkl\",\nlimit_check_report_keys = {\n- \"column_one\": {\n- \"min\": 0.5, #Indicating the threshold for the 'min' statistic in 'column_one' is 50%\n},\n- \"*\": {\n- \"*\": .25, #Indicating the threshold for every statistic in every column is 25%\n},\n}\n)\n- Note: In limit_check_report_keys, \"*\" in place of a column denotes a general operator in which the value it stores will be applied to every column in the data that has no explicit key.\n\"*\" in place of a statistic denotes a general operator in which the bounds it stores will be applied to every statistic for the given column that has no explicit key.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "data_profiler.profile_numeric_columns_percent_diff_less_than_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a statistic's percent delta for a given column of a DataProfiler percent difference report to be less than the specified threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_column_chisquare_simple_test_p_value_to_be_greater_than": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-07-20 13:06:00 -0500 13 days ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_chisquare_simple_test_p_value_to_be_greater_than.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_chisquare_simple_test_p_value_to_be_greater_than", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "statistical", "test", "testing" ], "contributors": [ "@HaebichanGX" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnChisquareSimpleTestPValueToBeGreaterThan", "snake_name": "expect_column_chisquare_simple_test_p_value_to_be_greater_than", "short_description": "Expect the chi-squared of 2 columns to have a p-value greater than the provided threshold.", "docstring": "Expect the chi-squared of 2 columns to have a p-value greater than the provided threshold." }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column.p_value_greater_than_threshold", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 0, "num_failed": 2, "failing_names": [ "basic_positive_test", "basic_negative_test" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "Only 0 / 2 tests for pandas are passing", "passed": false }, { "message": " - Failing: basic_positive_test, basic_negative_test", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the chi-squared of 2 columns to have a p-value greater than the provided threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": false, "doc_url": null, "sub_messages": [] } ] }, "coverage_score": -1.0 }, "expect_column_discrete_entropy_to_be_between": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2021-08-18 13:26:02 -0400 1 year, 11 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_discrete_entropy_to_be_between.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_discrete_entropy_to_be_between", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental" ], "contributors": [ "@edjoesu" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnDiscreteEntropyToBeBetween", "snake_name": "expect_column_discrete_entropy_to_be_between", "short_description": "Expect the column discrete entropy to be between a minimum value and a maximum value.", "docstring": "Expect the column discrete entropy to be between a minimum value and a maximum value.\nThe Shannon entropy of a discrete probability distribution is given by\n- \\sum_{i=1}^{n} P(x_i) * \\log(P(x_i))\nwhere P(x_i) is the probability of occurrence of value x_i.\nFor observed data the P(x_i) are replaced by the empirical frequencies n_{x_i}/N\n\nexpect_column_discrete_entropy_to_be_between is a :func:`column_aggregate_expectation\n`.\n\n#### Args:\n- column (str): The column name.\n- min_value (float or None): The minimum value for the column standard deviation.\n- max_value (float or None): The maximum value for the column standard deviation.\n\n#### strict_min (boolean):\nIf True, the column standard deviation must be strictly larger than min_value, default=False\n\n#### strict_max (boolean):\nIf True, the column standard deviation must be strictly smaller than max_value, default=False\n\n#### base (float):\nThe base of the logarithm, default=2\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: `BOOLEAN_ONLY`, `BASIC`, `COMPLETE`, or `SUMMARY`. For more detail, see :ref:`result_format `.\n- include_config (boolean): If True, then include the expectation config as part of the result object. For more detail, see :ref:`include_config`.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see :ref:`catch_exceptions`.\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see :ref:`meta`.\n\n#### Returns:\nAn ExpectationSuiteValidationResult\nExact fields vary depending on the values passed to :ref:`result_format ` and\n:ref:`include_config`, :ref:`catch_exceptions`, and :ref:`meta`.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.discrete.entropy", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 3, "num_failed": 1, "failing_names": [ "test_empty_column_should_be_zero" ] }, { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "Only 3 / 4 tests for spark are passing", "passed": false }, { "message": " - Failing: test_empty_column_should_be_zero", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column discrete entropy to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 9.5 }, "expect_column_distinct_values_to_be_continuous": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-02-17 00:14:33 +0000 6 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_distinct_values_to_be_continuous.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_distinct_values_to_be_continuous", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@jmoskovc" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnDistinctValuesToBeContinuous", "snake_name": "expect_column_distinct_values_to_be_continuous", "short_description": "Expect the set of distinct column values to be continuous.", "docstring": "Expect the set of distinct column values to be continuous." }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.value_counts", "has_question_renderer": false }, { "name": "column.max", "has_question_renderer": false }, { "name": "column.min", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the set of distinct column values to be continuous.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_column_distribution_to_match_benfords_law": { "updated_at": "2023-04-12 09:23:16 -0600 4 months ago", "created_at": "2022-03-21 11:49:59 -0500 1 year, 4 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_distribution_to_match_benfords_law.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_distribution_to_match_benfords_law", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental" ], "contributors": [ "@shekark642", "@vinodkri1" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnDistributionToMatchBenfordsLaw", "snake_name": "expect_column_distribution_to_match_benfords_law", "short_description": "Expect column distribution to match Benford's Law.", "docstring": "Expect column distribution to match Benford's Law.\n\nTests whether data matches Benford's Law Fraud Detection Algorithm. Uses a Chi-Square Goodness of Fit test with an 80@ p-value.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column.custom.DistributionMatchesBenfordsLaw", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column distribution to match Benford's Law.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_kurtosis_to_be_between": { "updated_at": "2023-06-02 19:15:21 -0400 9 weeks ago", "created_at": "2021-02-22 12:26:56 -0700 2 years, 5 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_kurtosis_to_be_between.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_kurtosis_to_be_between", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [], "contributors": [ "@lodeous", "@bragleg", "@rexboyce", "@mkopec87" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnKurtosisToBeBetween", "snake_name": "expect_column_kurtosis_to_be_between", "short_description": "Expect column Kurtosis to be between. 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"requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesNotToBeFutureDate", "snake_name": "expect_column_values_not_to_be_future_date", "short_description": "Expect column values not to be the future date.", "docstring": "Expect column values not to be the future date." }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.valid_date.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.valid_date.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { 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"message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column values not to be the future date.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_number_of_decimal_places_to_equal": { "updated_at": "2023-02-02 21:45:27 -0500 6 months ago", "created_at": "2021-01-28 15:09:39 -0600 2 years, 6 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_number_of_decimal_places_to_equal.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_number_of_decimal_places_to_equal", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental", "precision", "formatting", "floating_point", "hackathon-20200123" ], "contributors": [ "@samsonq", "@spbail", "@Lord-of-Bugs", "@BladderBoy", "@Rim921" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesNumberOfDecimalPlacesToEqual", "snake_name": "expect_column_values_number_of_decimal_places_to_equal", "short_description": "Expect all values in a numeric column to have the same number of specified decimal places.", "docstring": "Expect all values in a numeric column to have the same number of specified decimal places.\n\nThis expectation tests if all the values in a column has the same number of decimal places as the\ninputted number of decimal places. In the case where the decimal places are all 0s (an integer),\nthe value automatically passes. 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Comparison is case-insensitive. Using `mostly` will give you how many items are alphabetical\nrelative to the immediately previous item in the list.\n\n#### Args:\n- column (str): The column name\n\n#### Keyword Args:\n- reverse (boolean): Checks for Z to A alphabetical if True, otherwise checks A to Z\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.are_alphabetical.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.are_alphabetical.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect values to be alphabetical.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 7.0 }, "expect_column_values_to_be_edtf_parseable": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2021-02-19 04:52:20 +0100 2 years, 5 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_edtf_parseable.py", "package": "great_expectations_experimental", "requirements": [ "edtf_validate" ], "import_module_args": [ "expectations.expect_column_values_to_be_edtf_parseable", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "edtf", "datetime", "glam" ], "contributors": [ "@mielvds" ], "requirements": [ "edtf_validate" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeEdtfParseable", "snake_name": "expect_column_values_to_be_edtf_parseable", "short_description": "Expect column entries to be parsable using the [Extended Date/Time Format (EDTF) specification](https://www.loc.gov/standards/datetime/).", "docstring": "Expect column entries to be parsable using the [Extended Date/Time Format (EDTF) specification](https://www.loc.gov/standards/datetime/).\n\nexpect_column_values_to_be_edtf_parseable is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- level (int or None): The EDTF level to comply to.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.edtf_parseable.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.edtf_parseable.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 1, "failing_names": [ "positive_test_with_mostly" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "Only 4 / 5 tests for pandas are passing", "passed": false }, { "message": " - Failing: 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months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_be_hexadecimal.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_to_be_hexadecimal", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib RegexBasedColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "experimental" ], "contributors": [ "@andrewsx", "@mkopec87" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeHexadecimal", "snake_name": "expect_column_values_to_be_hexadecimal", "short_description": "Expect column values to be valid hexadecimals.", "docstring": "Expect column values to be valid hexadecimals." }, 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For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. 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one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column values to match powers of Base (Base ** power == column value).\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 7.0 }, "expect_column_values_to_match_thai": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-03-02 22:50:09 +0700 5 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_match_thai.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_to_match_thai", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib RegexBasedColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "regex", "thai" ], "contributors": [ "@swittchawa" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchThai", "snake_name": "expect_column_values_to_match_thai", "short_description": "Expect column values to contain Thai Language.", "docstring": "Expect column values to contain Thai Language.\n\n#### Args:\n- column (str): A integer column that consist of Thai language.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": 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"passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 9.0 }, "expect_column_values_to_not_be_null_and_column_to_not_be_empty": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2023-03-27 12:14:30 -0300 4 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_not_be_null_and_column_to_not_be_empty.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_to_not_be_null_and_column_to_not_be_empty", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental", "column map expectation" ], "contributors": [ "@tmilitino" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotBeNullAndColumnToNotBeEmpty", "snake_name": "expect_column_values_to_not_be_null_and_column_to_not_be_empty", "short_description": "Expect column values to not be null and column to not be empty.", "docstring": "Expect column values to not be null and column to not be empty.\n\nTo be counted as an exception, values must be explicitly null or missing, such as a NULL in PostgreSQL or an\nnp.NaN in pandas. Empty strings don't count as null unless they have been coerced to a null type.\n\nexpect_column_values_to_not_be_null_and_column_to_not_be_empty is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_be_null](https://greatexpectations.io/expectations/expect_column_values_to_be_null)\n- [expect_column_values_not_to_be_null](https://greatexpectations.io/expectations/expect_column_values_not_to_be_null)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.nonnull.unexpected_values", "has_question_renderer": false } ], 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21:45:27 -0500 6 months ago", "created_at": "2021-02-22 12:26:56 -0700 2 years, 5 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_not_be_outliers.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_to_not_be_outliers", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [], "contributors": [ "@rexboyce", "@lodeous", "@bragleg" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotBeOutliers", "snake_name": "expect_column_values_to_not_be_outliers", "short_description": "Expect Column Values to not be outliers.", "docstring": "Expect Column Values to not be outliers.\n\n#### Args:\n- column (str): The column name\n\n#### Keyword Args:\n- method (str): Either \"std\" (standard deviation) or \"iqr\" (inter-quantile range)\n- multiplier (float): multiplier\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_outliers.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_outliers.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of 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short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect Column Values to not be outliers.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_column_values_to_not_contain_character": { "updated_at": "2023-02-02 21:45:27 -0500 6 months ago", "created_at": "2021-01-29 09:40:37 -0800 2 years, 6 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_not_contain_character.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_to_not_contain_character", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental", "hackathon-20200123" ], "contributors": [ "@jsteinberg4", "@vraimondi04", "@talagluck" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotContainCharacter", "snake_name": "expect_column_values_to_not_contain_character", "short_description": "Expect the set of column values to not contain a given character.", "docstring": "Expect the set of column values to not contain a given character.\n\nexpect_column_values_to_not_contain_character is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The provided column name\n- character (str): A character to test for the nonexistence of\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_contain_character.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_contain_character.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", 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Failing: test_for_at_symbol", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the set of column values to not contain a given character.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": false, "doc_url": null, "sub_messages": [] } ] }, "coverage_score": 3.5 }, "expect_column_values_to_not_contain_special_characters": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2021-12-01 11:14:18 +1300 1 year, 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_values_to_not_contain_special_characters.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_values_to_not_contain_special_characters", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental expectation", "column map expectation", "special characters" ], "contributors": [ "@jaibirsingh" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotContainSpecialCharacters", "snake_name": "expect_column_values_to_not_contain_special_characters", "short_description": "Expect column entries to not contain special characters.", "docstring": "Expect column entries to not contain special characters.\n\n#### Args:\n- column (str): The column name\n\n#### Keyword Args:\n- mostly (None or a float value between 0 and 1): Successful if at least mostly fraction of values match the expectation For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_contain_special_character.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": 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for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 1 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column entries to not contain special characters.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 1 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 3.0 }, "expect_column_wasserstein_distance_to_be_less_than": { "updated_at": "2023-06-02 19:15:21 -0400 9 weeks ago", "created_at": "2021-01-22 20:03:05 -0700 2 years, 6 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_column_wasserstein_distance_to_be_less_than.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_column_wasserstein_distance_to_be_less_than", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [], "contributors": [ "rexboyce", "abegong", "lodeous" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnWassersteinDistanceToBeLessThan", "snake_name": "expect_column_wasserstein_distance_to_be_less_than", "short_description": "Expect that the Wasserstein Distance of the specified column with respect to an optional partition object to be lower than the provided value.", "docstring": "Expect that the Wasserstein Distance of the specified column with respect to an optional partition object to be lower than the provided value.\n\n#### See Also:\n- [partition objects for distributional Expectations](https://docs.greatexpectations.io/docs/reference/expectations/distributional_expectations/#partition-objects)\n- [Wasserstein Metric on Wikipedia](https://en.wikipedia.org/wiki/Wasserstein_metric)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column.custom.wasserstein", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that the Wasserstein Distance of the specified column with respect to an optional partition object to be lower than the provided value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 5.0 }, "expect_day_sum_to_be_close_to_equivalent_week_day_mean": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-12-30 19:45:22 +0200 7 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_day_sum_to_be_close_to_equivalent_week_day_mean.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_day_sum_to_be_close_to_equivalent_week_day_mean", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "query-based" ], "contributors": [ "@itaise", "@hadasm" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectDaySumToBeCloseToEquivalentWeekDayMean", "snake_name": "expect_day_sum_to_be_close_to_equivalent_week_day_mean", "short_description": "Expect the daily sums of the given column to be close to the average sums calculated 4 weeks back.", "docstring": "Expect the daily sums of the given column to be close to the average sums calculated 4 weeks back.\n\nThis metric expects daily sums of the given column, to be close to the average sums calculated 4 weeks back,\nrespective to the specific day of the week.\nThe expectation fails if the difference in percentage ((current_sum - average_sum) / average_sum) is more than the\nthreshold given by user (default value is 25%).\nThe threshold parameter should be given in fraction and not percent, i.e. for 25% define threshold = 0.25.\n\n#### Keyword args:\n- - threshold (float; default = 0.25): threshold of difference between current and past weeks over which expectation fails\n- - weeks_back (int; default = 4): how many weeks back to compare the current metric with\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": 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manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the daily sums of the given column to be close to the average sums calculated 4 weeks back.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 50.0 }, "expect_foreign_keys_in_column_a_to_exist_in_column_b": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-03-21 11:49:59 -0500 1 year, 4 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_foreign_keys_in_column_a_to_exist_in_column_b.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_foreign_keys_in_column_a_to_exist_in_column_b", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental", "help_wanted" ], "contributors": [ "@robertparker" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectForeignKeysInColumnAToExistInColumnB", "snake_name": "expect_foreign_keys_in_column_a_to_exist_in_column_b", "short_description": "Expect foreign keys in a column to exist in another specified column.", "docstring": "Expect foreign keys in a column to exist in another specified column.\n\nEnsure that values in the column of interest (ColumnA) are in a valueset provided as a dataframe (df parameter) + column (column_B parameter) or as a list of elements supported by pandas.DataFrame() (e.g. list of dicts [{\"col_name\": value},], list of tuples [(value, value), (value, value)]. This is a very experimental implementation to describe the functionality, but this expectation should be revisited once cross-table expectation templates are available.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.foreign_key_in_other_col.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.foreign_key_in_other_col.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect foreign keys in a column to exist in another specified column.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_multicolumn_datetime_difference_in_months": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2023-04-08 00:23:51 +1000 4 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_datetime_difference_in_months.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_multicolumn_datetime_difference_in_months", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "multi-column expectation", "multi-column column datetime difference in months" ], "contributors": [ "@tb102122" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectMulticolumnDatetimeDifferenceInMonths", "snake_name": "expect_multicolumn_datetime_difference_in_months", "short_description": "Expect the difference of 2 datetime columns is equal to another column in month.", "docstring": "Expect the difference of 2 datetime columns is equal to another column in month.\n\nThis means that for each row, we expect end_datetime - start_datetime = gap (in months)\n\n#### Args:\n\nstart_datetime (datetime): The first datetime column to compare.\nend_datetime (datetime): The second datetime column to compare.\ngap (int): The number of months that the difference between start_datetime and end_datetime should be.\n\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "multicolumn_values.column_datetime_difference_in_months.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "multicolumn_values.column_datetime_difference_in_months.filtered_row_count", "has_question_renderer": false }, { "name": "multicolumn_values.column_datetime_difference_in_months.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the difference of 2 datetime columns is equal to another column in month.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_multicolumn_sum_values_to_be_between": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-03-01 21:44:55 +0700 5 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_sum_values_to_be_between.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_multicolumn_sum_values_to_be_between", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "multi-column sum to be between min and maxmulti-column expectation" ], "contributors": [ "@swittchawa" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectMulticolumnSumValuesToBeBetween", "snake_name": "expect_multicolumn_sum_values_to_be_between", "short_description": "Expect a sum of values over the columns to be between max and min values.", "docstring": "Expect a sum of values over the columns to be between max and min values.\n\nmin_value <= SUM(col_a, cob_b, cob_c, ...) <= max_value\n\n#### Args:\n- column_list (list of str): A list of 2 or more integer columns\n- min_value (int): A value that the sum of values over the column must be equal to or more than the given value\n- max_value (int): A value that the sum of values over the column must be equal to or less than the given value\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "multicolumn_values.sum_values_to_be_between_max_and_min.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "multicolumn_values.sum_values_to_be_between_max_and_min.filtered_row_count", "has_question_renderer": false }, { "name": "multicolumn_values.sum_values_to_be_between_max_and_min.unexpected_values", 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"message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a sum of values over the columns to be between max and min values.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 12.0 }, "expect_multicolumn_sum_values_to_be_equal_to_single_column": { "updated_at": "2023-04-19 09:29:12 -0700 4 months ago", "created_at": "2022-12-01 00:06:05 +0200 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_sum_values_to_be_equal_to_single_column.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_multicolumn_sum_values_to_be_equal_to_single_column", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "multi-column expectation", "multi-column sum values to be equal to single column" ], "contributors": [ "@AsaFLachisch", "@mkopec87" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectMulticolumnSumValuesToBeEqualToSingleColumn", "snake_name": "expect_multicolumn_sum_values_to_be_equal_to_single_column", "short_description": "Expect a sum of columns to be equal to other column (in a row perspective).", "docstring": "Expect a sum of columns to be equal to other column (in a row perspective).\n\nThis means that for each row, we expect col_a + col_b + ... + col_n-1 == col_n\n\n#### Args:\n- column_list (list of str): A list of 2 or more integer columns, in which we expect the sum of the first n-1th columns to be equal to the nth column. This means that if one wants to compare between the sum of n-1 columns and the nth column, it needs to put the nth column at the end of the list.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "multicolumn_values.sum_values_equal_to_single_column.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "multicolumn_values.sum_values_equal_to_single_column.filtered_row_count", "has_question_renderer": false }, { "name": "multicolumn_values.sum_values_equal_to_single_column.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "trino", 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spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a sum of columns to be equal to other column (in a row perspective).\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 53.0 }, "expect_multicolumn_values_not_to_be_all_null": { "updated_at": "2023-06-15 11:26:21 -0500 7 weeks ago", "created_at": "2023-01-30 13:42:21 -0500 6 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_multicolumn_values_not_to_be_all_null.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_multicolumn_values_not_to_be_all_null", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "null_check" ], "contributors": [ "@liyusa" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectMulticolumnValuesNotToBeAllNull", "snake_name": "expect_multicolumn_values_not_to_be_all_null", "short_description": "Expect the certain set of columns not to be null at the same time.", "docstring": "Expect the certain set of columns not to be null at the same time." }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "multicolumn_values.not_all_null.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "multicolumn_values.not_all_null.filtered_row_count", "has_question_renderer": false }, { "name": "multicolumn_values.not_all_null.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the certain set of columns not to be null at the same time.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 5.0 }, "expect_queried_column_list_to_be_unique": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2022-12-02 17:40:18 +0200 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_list_to_be_unique.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_list_to_be_unique", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "query-based" ], "contributors": [ "@itaise", "@maayaniti" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnListToBeUnique", "snake_name": "expect_queried_column_list_to_be_unique", "short_description": "Expect multiple columns (such as a compound key) to be unique.", "docstring": "Expect multiple columns (such as a compound key) to be unique.\n\n#### Args:\n- template_dict: dict with the following keys: column_list (columns to check uniqueness on separated by comma)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "trino", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for trino are passing", "passed": true }, { "message": "All 3 tests for redshift are passing", "passed": true }, { "message": "All 3 tests for bigquery are passing", "passed": true }, { "message": "All 3 tests for spark are passing", "passed": true }, { "message": "All 3 tests for sqlite are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect multiple columns (such as a compound key) to be unique.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for trino are passing", "passed": true }, { "message": "All 3 tests for redshift are passing", "passed": true }, { "message": "All 3 tests for bigquery are passing", "passed": true }, { "message": "All 3 tests for spark are passing", "passed": true }, { "message": "All 3 tests for sqlite are passing", "passed": true } ] } ] }, "coverage_score": 22.0 }, "expect_queried_column_pair_values_to_be_both_filled_or_null": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-06-21 02:13:29 +0300 6 weeks ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_pair_values_to_be_both_filled_or_null.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_pair_values_to_be_both_filled_or_null", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "query-based" ], "contributors": [ "@eden-o" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnPairValuesToBeBothFilledOrNull", "snake_name": "expect_queried_column_pair_values_to_be_both_filled_or_null", "short_description": "Expect the values of a pair of columns to be either both filled or empty simultaneously.", "docstring": "Expect the values of a pair of columns to be either both filled or empty simultaneously.\n\nIt checks if 2 columns are aligned - the values of each row need to either be both empty or filled.\nThe expectation will fail if there's at least one row where one column is filled and the other isn't.\n\n#### Args:\n- template_dict: dict with the following keys: column_a (str): first column name, to compare values against column_b\n- column_b (str): second column name, to compare values against column_a\n\n#### Returns:\nNone\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "sqlite", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for sqlite are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the values of a pair of columns to be either both filled or empty simultaneously.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for sqlite are passing", "passed": true } ] } ] }, "coverage_score": 7.0 }, "expect_queried_column_pair_values_to_have_diff": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-08-21 17:20:32 -0600 12 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_pair_values_to_have_diff.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_pair_values_to_have_diff", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "query-based" ], "contributors": [ "@austiezr" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnPairValuesToHaveDiff", "snake_name": "expect_queried_column_pair_values_to_have_diff", "short_description": "Expect the frequency of occurrences of a specified value in a queried column to be at least percent of values in that column.", "docstring": "Expect the frequency of occurrences of a specified value in a queried column to be at least percent of values in that column." }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.column_pair", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the frequency of occurrences of a specified value in a queried column to be at least percent of values in that column.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true } ] } ] }, "coverage_score": 10.0 }, "expect_queried_column_to_be_unique_with_condition": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2022-11-22 19:23:12 +0200 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_to_be_unique_with_condition.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_to_be_unique_with_condition", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "query-based" ], "contributors": [ "@itaise" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnToBeUniqueWithCondition", "snake_name": "expect_queried_column_to_be_unique_with_condition", "short_description": "Expect column values to be unique, with an filter.", "docstring": "Expect column values to be unique, with an filter.\n\n#### Args:\n- template_dict: dict with the following keys: column_to_check (column to check uniqueness on. can be multiple column names separated by comma), condition (the filter for boolean column, you can provide just the column name evaluated to True)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column values to be unique, with an filter.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_queried_column_to_have_n_distinct_values_with_condition": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2022-11-29 20:21:06 +0200 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_to_have_n_distinct_values_with_condition.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_to_have_n_distinct_values_with_condition", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "query-based" ], "contributors": [ "@itaise" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnToHaveNDistinctValuesWithCondition", "snake_name": "expect_queried_column_to_have_n_distinct_values_with_condition", "short_description": "Expect a column to have N distinct values, with an filter.", "docstring": "Expect a column to have N distinct values, with an filter.\n\n#### Args:\n- template_dict: dict with the following keys: column_to_check (column to check uniqueness on. can be multiple column names separated by comma), condition (the filter for boolean column, you can provide just the column name, evaluated to True), num_of_distinct_values (number of distinct values the column is supposed ot have)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 0, "num_failed": 2, "failing_names": [ "basic_positive_test", "basic_negative_test" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "Only 0 / 2 tests for sqlite are passing", "passed": false }, { "message": " - Failing: basic_positive_test, basic_negative_test", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a column to have N distinct values, with an filter.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true } ] } ] }, "coverage_score": 3.0 }, "expect_queried_column_value_frequency_to_meet_threshold": { "updated_at": "2023-04-24 13:49:26 -0400 3 months ago", "created_at": "2022-07-08 11:28:04 -0600 1 year, 1 month ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_value_frequency_to_meet_threshold.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_value_frequency_to_meet_threshold", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "query-based" ], "contributors": [ "@austiezr", "@mkopec87" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnValueFrequencyToMeetThreshold", "snake_name": "expect_queried_column_value_frequency_to_meet_threshold", "short_description": "Expect the frequency of occurrences of a specified value in a queried column to be at least percent of values in that column.", "docstring": "Expect the frequency of occurrences of a specified value in a queried column to be at least percent of values in that column." }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.column", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "postgresql", "num_passed": 4, "num_failed": 1, "failing_names": [ "multi_value_positive_test_static_data_asset" ] }, { "backend": "mssql", "num_passed": 4, "num_failed": 1, "failing_names": [ "multi_value_positive_test_static_data_asset" ] }, { "backend": "spark", "num_passed": 4, "num_failed": 1, "failing_names": [ "multi_value_positive_test_static_data_asset" ] }, { "backend": "sqlite", "num_passed": 4, "num_failed": 1, "failing_names": [ "multi_value_positive_test_static_data_asset" ] }, { "backend": "mysql", "num_passed": 4, "num_failed": 1, "failing_names": [ "multi_value_positive_test_static_data_asset" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "Only 4 / 5 tests for postgresql are passing", "passed": false }, { "message": " - Failing: multi_value_positive_test_static_data_asset", "passed": false }, { "message": "Only 4 / 5 tests for mssql are passing", "passed": false }, { "message": " - Failing: multi_value_positive_test_static_data_asset", "passed": false }, { "message": "Only 4 / 5 tests for spark are passing", "passed": false }, { "message": " - Failing: multi_value_positive_test_static_data_asset", "passed": false }, { "message": "Only 4 / 5 tests for sqlite are passing", "passed": false }, { "message": " - Failing: multi_value_positive_test_static_data_asset", "passed": false }, { "message": "Only 4 / 5 tests for mysql are passing", "passed": false }, { "message": " - Failing: multi_value_positive_test_static_data_asset", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the frequency of occurrences of a specified value in a queried column to be at least percent of values in that column.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": false, "doc_url": null, "sub_messages": [] } ] }, "coverage_score": 19.5 }, "expect_queried_column_values_to_exist_in_second_table_column": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2022-11-29 20:30:04 +0200 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_column_values_to_exist_in_second_table_column.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_column_values_to_exist_in_second_table_column", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "query-based" ], "contributors": [ "@itaise" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedColumnValuesToExistInSecondTableColumn", "snake_name": "expect_queried_column_values_to_exist_in_second_table_column", "short_description": "Expect all values in a specific column to exist in another table's column.", "docstring": "Expect all values in a specific column to exist in another table's column.\n\n#### Args:\n- template_dict: dict containing the following keys: first_table_column (name of the main table column), second_table_column (name of the column to compare to in the second table), second_table_full_name, condition (additional condition added in the where clause, provide \"1=1\" if not needed)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "redshift", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 0, "num_failed": 2, "failing_names": [ "basic_negative_test", "basic_positive_test" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "Only 0 / 2 tests for sqlite are passing", "passed": false }, { "message": " - Failing: basic_negative_test, basic_positive_test", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect all values in a specific column to exist in another table's column.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for redshift are passing", "passed": true } ] } ] }, "coverage_score": 3.0 }, "expect_queried_custom_query_to_return_num_rows": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2023-03-23 10:28:07 +0200 4 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_custom_query_to_return_num_rows.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_custom_query_to_return_num_rows", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "query-based" ], "contributors": [ "@mantasmy", "@itaise" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedCustomQueryToReturnNumRows", "snake_name": "expect_queried_custom_query_to_return_num_rows", "short_description": "Expect the number of rows returned from custom query to be equal to specified value.", "docstring": "Expect the number of rows returned from custom query to be equal to specified value.\n\n#### Args:\n- template_dict: dict containing the following key: user_query (user query. It must contain active_batch e.g. \"select * from {active_batch}\")\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 0, "num_failed": 2, "failing_names": [ "basic_positive_test", "basic_negative_test" ] }, { "backend": "redshift", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 0, "num_failed": 2, "failing_names": [ "basic_positive_test", "basic_negative_test" ] }, { "backend": "bigquery", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 0, "num_failed": 2, "failing_names": [ "basic_positive_test", "basic_negative_test" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for snowflake are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 2 tests for bigquery are passing", "passed": true }, { "message": "Only 0 / 2 tests for trino are passing", "passed": false }, { "message": " - Failing: basic_positive_test, basic_negative_test", "passed": false }, { "message": "Only 0 / 2 tests for postgresql are passing", "passed": false }, { "message": " - Failing: basic_positive_test, basic_negative_test", "passed": false }, { "message": "Only 0 / 2 tests for sqlite are passing", "passed": false }, { "message": " - Failing: basic_positive_test, basic_negative_test", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of rows returned from custom query to be equal to specified value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for snowflake are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 2 tests for bigquery are passing", "passed": true } ] } ] }, "coverage_score": 5.0 }, "expect_queried_slowly_changing_table_to_have_no_gaps": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2022-11-29 20:50:18 +0200 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_slowly_changing_table_to_have_no_gaps.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_slowly_changing_table_to_have_no_gaps", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "query-based" ], "contributors": [ "@itaise" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedSlowlyChangingTableToHaveNoGaps", "snake_name": "expect_queried_slowly_changing_table_to_have_no_gaps", "short_description": "Expect Slowly changing table type II to have no gaps between the 'end date' of each row, and the next 'start date' in the next row.", "docstring": "Expect Slowly changing table type II to have no gaps between the 'end date' of each row, and the next 'start date' in the next row.\n\n#### Args:\n- template_dict: dict with the following keys: primary_key (primary key column name or multiple columns, comma separated), open_date_column (name of the column representing open date), close_date_column (name of the column representing clode date)\n- threshold: an optional parameter - default is zero. if the ratio of \"gaps\" to total table rows is higher than threshold - error will be raised.\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "trino", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for trino are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect Slowly changing table type II to have no gaps between the 'end date' of each row, and the next 'start date' in the next row.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for trino are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true } ] } ] }, "coverage_score": 14.0 }, "expect_queried_table_row_count_to_be": { "updated_at": "2023-04-12 08:43:18 -0500 4 months ago", "created_at": "2022-07-08 11:28:04 -0600 1 year, 1 month ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_queried_table_row_count_to_be.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_queried_table_row_count_to_be", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "query-based" ], "contributors": [ "@austiezr" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueriedTableRowCountToBe", "snake_name": "expect_queried_table_row_count_to_be", "short_description": "Expect the expect the number of rows returned from a queried table to equal a specified value.", "docstring": "Expect the expect the number of rows returned from a queried table to equal a specified value." }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.table", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "trino", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "redshift", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "postgresql", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "bigquery", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "mssql", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "spark", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "sqlite", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] }, { "backend": "mysql", "num_passed": 3, "num_failed": 1, "failing_names": [ "positive_test_static_data_asset" ] } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "Only 3 / 4 tests for trino are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for redshift are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for postgresql are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for bigquery are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for mssql are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for spark are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for sqlite are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false }, { "message": "Only 3 / 4 tests for mysql are passing", "passed": false }, { "message": " - Failing: positive_test_static_data_asset", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the expect the number of rows returned from a queried table to equal a specified value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": false, "doc_url": null, "sub_messages": [] } ] }, "coverage_score": 22.0 }, "expect_query_count_with_filter_to_meet_threshold": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-11-29 22:48:18 +0100 8 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_query_count_with_filter_to_meet_threshold.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_query_count_with_filter_to_meet_threshold", "great_expectations_experimental" ], "sys_path": "../../contrib/experimental/great_expectations_experimental", "exp_type": "Contrib QueryExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "query-based" ], "contributors": [ "@CarstenFrommhold", "@mkopec87" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectQueryCountWithFilterToMeetThreshold", "snake_name": "expect_query_count_with_filter_to_meet_threshold", "short_description": "Expect Query given filter to contain at least as many entries as a given threshold.", "docstring": "Expect Query given filter to contain at least as many entries as a given threshold." }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "query.template_values", "has_question_renderer": false } ], "errors": [], 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columns to be unique.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for spark are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_table_checksum_to_equal_other_table": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2022-04-23 00:40:52 +0530 1 year, 3 months ago", "path": "contrib/experimental/great_expectations_experimental/expectations/expect_table_checksum_to_equal_other_table.py", "package": "great_expectations_experimental", "requirements": [], "import_module_args": [ "expectations.expect_table_checksum_to_equal_other_table", "great_expectations_experimental" ], "sys_path": 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other_table_name (str): The name of the other table.\n- ignore_columns (str): optional: Comma seperated list of columns which should be ignored from checksum calculation. e.g. \"created_date, userid\"\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. 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"sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "Only 0 / 2 tests for bigquery are passing", "passed": false }, { "message": " - Failing: for bigquery - positive_test_with_checksum_equal, for bigquery - negative_test_with_checksum_not_equal", "passed": false }, { "message": "Only 1 / 4 tests for sqlite are passing", "passed": false }, { "message": " - Failing: for sqlite - positive_test_with_checksum_equal_with_ignore_columns, for sqlite - negative_test_with_checksum_not_equal_with_ignore_columns, for sqlite - negative_test_with_checksum_not_equal_without_ignore_columns", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", 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tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a specific value at a given index location within each element of the column.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_table_binary_label_model_bias": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_ethical_ai_expectations/great_expectations_ethical_ai_expectations/expectations/expect_table_binary_label_model_bias.py", "package": "great_expectations_ethical_ai_expectations", "requirements": [ "aequitas" ], "import_module_args": [ "great_expectations_ethical_ai_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_ethical_ai_expectations", "exp_type": "Contrib BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "ai/ml", "fair-ai", "hackathon-22" ], "contributors": [ "@luismdiaz01", "@derekma73" ], "requirements": [ "aequitas" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectTableBinaryLabelModelBias", "snake_name": "expect_table_binary_label_model_bias", "short_description": "Expect fairness in a model by calculating disparities among features, score (binary or continuous), and a label (binary) in a table using Aequitas.", "docstring": "Expect fairness in a model by calculating disparities among features, score (binary or continuous), and a label (binary) in a table using Aequitas.\n\nUsing Aeqitas we evaluate predicted and true values to evaluate certain metrics on how a classider model imposes bias on a given attribute group. Requires columns score (binary or continuous) and label_value (binary). For more information go to https://dssg.github.io/aequitas/examples/compas_demo.html\n\nexpect_table_binary_label_model_bias is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- y_true (str): The column name of the actual y vlaue. Must be binary\n- y_pred (str): The column name of the modeled y value. Must be binary or continuous\n\n#### Keyword Args:\n- partial_success (boolean): If True, expectations will pass if supervised or supervised fairness are observed even if overall fairness was false.\n- reference_group (dict): A JSON-serializable dictionary (nesting allowed) that will be used to compare in reference to the group specified. Ex: {'race':'Caucasian', 'sex':'Male', 'age_cat':'25 - 45'}.\n- alpha (float): A float between 0 and 1 that determines statistical significance level to use in significance determination. Default is .05\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "table.modeling.binary.model_bias", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, 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"contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_average_lat_lon_pairwise_distance_to_be_less_than.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "scipy", "scikit-learn", "numpy" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "geospatial", "hackathon-22" ], "contributors": [ "@mmi333" ], "requirements": [ "scipy", "scikit-learn", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnAverageLatLonPairwiseDistanceToBeLessThan", "snake_name": "expect_column_average_lat_lon_pairwise_distance_to_be_less_than", "short_description": "Expect the average pairwise haversine distance between lat/lon points in a column is less than some value in km.", "docstring": "Expect the average pairwise haversine distance between lat/lon points in a column is less than some value in km.\n\nThis expectation will compute the pairwise haversine distance between each (latitude, longitude) pair and test that the average is less than some value in km.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column.average_lat_lon_pairwise_distance", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, 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"manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnMinimumBoundingRadiusToBeBetween", "snake_name": "expect_column_minimum_bounding_radius_to_be_between", "short_description": "Expect that column values as geometry points to be contained within a bounding circle with a given radius (or diameter).", "docstring": "Expect that column values as geometry points to be contained within a bounding circle with a given radius (or diameter).\n\nexpect_column_values_minimum_bounding_radius_to_be_between is a :func:`column_expectation `.\n\n#### Args:\n- column (str): The column name.\nColumn values must be provided in WKT or WKB format, which are commom formats for GIS Database formats.\nWKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\nColumn values can alternately be given in x,y tuple or list pairs.\nThe user is responsible for the coordinate reference system and the units. e.g. values may be given in easting-northing pairs.\n- min_value (float or None): The minimum radius (or diameter) that bounds all geometries in the column\n- max_value (float or None): The maximum radius (or diameter) that bounds all geometries in the column\n- strict_min (boolean): If True, the minimal radius must be strictly larger than min_value,\n- Default: False\n- strict_max (boolean): If True, the maximal radius must be strictly smaller than max_value,\n- Default: False\n\n#### Keyword Args:\n- column_shape_format: str\nGeometry format for 'column' (wkt, wkb, xy). Column values can be provided in WKT or WKB format, which are commom formats for GIS Database formats.\nxy also supports tuple pairs or list pairs for points only\nWKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n- Must be one of: [wkt, wkb, xy]\n- Default: wkt\n- diameter_flag (boolean): If True, the user can specify a diameter as opposed to a radius,\n- Default: False\n\n#### Returns:\nAn ExpectationSuiteValidationResult\n\n#### Notes:\n\n#### These fields in the result object are customized for this expectation:\n```\n{\n \"observed_value\": (list) The actual bounding radius (or diameter)\n}\n```\n\n\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column.geometry.minimum_bounding_radius", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometry points to be contained within a bounding circle with a given radius (or diameter).\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 5.0 }, "expect_column_pair_values_lat_lng_matches_geohash": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_pair_values_lat_lng_matches_geohash.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "python-geohash", "pandas" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnPairMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-22", "multi-column expectation" ], "contributors": [ "@chrisarnold91" ], "requirements": [ "python-geohash", "pandas" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnPairValuesLatLngMatchesGeohash", "snake_name": "expect_column_pair_values_lat_lng_matches_geohash", "short_description": "Expect latlngs in column A to match with geohashes in column B.", "docstring": "Expect latlngs in column A to match with geohashes in column B.\n\nThe more digits a geohash has, the smaller and more precise of an area it represents. When converting a latlng to a geohash, we are only asserting that it falls somewhere within the other geohash we're comparing it with. To verify this, we only need to make sure that they share their left-most digits. For example, dpz8 contains dpz80 (in addition to any other geohash that begins with \"dpz8\".\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_pair_values.lat_lng_matches_geohash.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_pair_values.lat_lng_matches_geohash.filtered_row_count", "has_question_renderer": false }, { "name": "column_pair_values.lat_lng_matches_geohash.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect latlngs in column A to match with geohashes in column B.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 5.0 }, "expect_column_values_geometry_centroids_to_be_within_shape": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_centroids_to_be_within_shape.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "pygeos" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@pjdobson" ], "requirements": [ "pygeos" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryCentroidsToBeWithinShape", "snake_name": "expect_column_values_geometry_centroids_to_be_within_shape", "short_description": "Expect that column values as geometries each have a centroid that are within a given reference shape.", "docstring": "Expect that column values as geometries each have a centroid that are within a given reference shape.\n\nexpect_column_values_geometry_centroids_to_be_within_shape is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n\n#### Keyword Args:\n- shape (str or list of str): The reference geometry\n- shape_format (str): Geometry format for 'shape' string(s). Can be provided as 'Well Known Text' (WKT), 'Well Known Binary' (WKB), or as GeoJSON. Must be one of: [wkt, wkb, geojson]. Default: wkt\n- column_shape_format (str): Geometry format for 'column'. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\n#### Notes:\n* Convention is (X Y Z) for points, which would map to (Longitude Latitude Elevation) for geospatial cases.\n* Any convention can be followed as long as the test and reference shapes are consistent.\n* The reference shape allows for an array, but will union (merge) all the shapes into 1 and check the contains condition.\n* MultiLinestrings and Multipolygons area weighted by their length and areas, respectively. See the pygeos docs for reference.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry.centroids_within_shape.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry.centroids_within_shape.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometries each have a centroid that are within a given reference shape.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_geometry_distance_to_address_to_be_between": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_distance_to_address_to_be_between.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "pygeos", "geopy" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@pjdobson" ], "requirements": [ "pygeos", "geopy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryDistanceToAddressToBeBetween", "snake_name": "expect_column_values_geometry_distance_to_address_to_be_between", "short_description": "Expect that column values as geometry points to be between a certain distance from a geocoded object.", "docstring": "Expect that column values as geometry points to be between a certain distance from a geocoded object.\n\nexpect_column_values_geometry_distance_to_address_to_be_between is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name. Column values must be provided in lon-lat or lat-long tuples/lists, WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL. Values must be in longitude - latitude coordinates for this method to work with geocoding.\n\n#### Keyword Args:\n- place (str): The country, place, address, etc. to query. Expect to return a point from geocoder (Default: OpenStreetMaps (Nominatim)). Note that this method uses the Latitude - Longitude Point returned by the geocoder, and not the shape(geometry)\n- column_shape_format (str): Geometry format for 'column' (wkt, wkb, lonlat, latlon). Column values can be provided in WKT or WKB format, which are commom formats for GIS Database formats. latlon or lonlat also supports tuple pairs or list pairs in either Longtitude or Latitude first formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n- geocoder (str): Geocoder from GeoPy to use to return the shape. While this is generic, the api is required to be available from GeoPy and must return a geometry.\n- geocoder_config (dict str): arguments to initialize the GeoPy geocoder. e.g. for paid services, an API_key is usually required. See GeoPy for reference.\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\n#### Notes:\n* The user is responsible to transform the column to a WKT or WKB format that is in the WGS84 coordianate system for earth.\n* Any geometry can be provided in the column value, but if it is not a POINT, the centroid will be evaluated.\n* Invalid geometries import as None and fail the test.\n* Other Coordinate Reference Systems are not yet supported.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry.distance_to_address.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry.distance_to_address.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometry points to be between a certain distance from a geocoded object.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_geometry_not_to_overlap": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_not_to_overlap.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "rtree", "geopandas", "shapely", "numpy" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "hackathon-22", "geospatial" ], "contributors": [ "@luismdiaz01", "@derekma73" ], "requirements": [ "rtree", "geopandas", "shapely", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryNotToOverlap", "snake_name": "expect_column_values_geometry_not_to_overlap", "short_description": "Expect geometries in this column Not to overlap with each other. If any two geometries do overlap, expectation will return False.", "docstring": "Expect geometries in this column Not to overlap with each other. If any two geometries do overlap, expectation will return False.\n\nFor more information look here https://stackoverflow.com/questions/64042379/shapely-is-valid-returns-true-to-invalid-overlap-polygons\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.geometry_not_overlap", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect geometries in this column Not to overlap with each other. If any two geometries do overlap, expectation will return False.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_geometry_to_be_near_shape": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_to_be_near_shape.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "pygeos" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@pjdobson" ], "requirements": [ "pygeos" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryToBeNearShape", "snake_name": "expect_column_values_geometry_to_be_near_shape", "short_description": "Expect that column values as geometries are near (within a given distance) of a given reference shape in the units of the provided geometries.", "docstring": "Expect that column values as geometries are near (within a given distance) of a given reference shape in the units of the provided geometries.\n\nexpect_column_values_geometry_to_be_near_shape is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n\n#### Keyword Args:\n- shape (str or list of str): The reference geometry\n- shape_format (str): Geometry format for 'shape' string(s). Can be provided as 'Well Known Text' (WKT), 'Well Known Binary' (WKB), or as GeoJSON. Must be one of: [wkt, wkb, geojson]. Default: wkt\n- column_shape_format (str): Geometry format for 'column'. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n- distance_tol (float): Distance tolerance for the column value geometries to the reference shape. Note that 0 evaluates to expect_column_values_to_be_within_shape. Distance values are always positive. Negative tolerances will always evaluate to False. Default: 0\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\n#### Notes:\n* The distance returned and specified is based on the coordinate system of the points, not Latitude and Longitude.\n* The user is responsible for the projection method and units (e.g. UTM in m).\n* The distance calculation is based on a cartesian coordinate system.\n* Convention is (X Y Z) for points, which would map to (Longitude Latitude Elevation) for geospatial cases.\n* Any convention can be followed as long as the test and reference shapes are consistent.\n* The reference shape allows for an array, but will union (merge) all the shapes into 1 and check the contains condition.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry.near_shape.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry.near_shape.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometries are near (within a given distance) of a given reference shape in the units of the provided geometries.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 7.0 }, "expect_column_values_geometry_to_be_of_type": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_to_be_of_type.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "shapely" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@mmi333" ], "requirements": [ "shapely" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryToBeOfType", "snake_name": "expect_column_values_geometry_to_be_of_type", "short_description": "Expect values in a column to belong to one of the specified geometry types.", "docstring": "Expect values in a column to belong to one of the specified geometry types.\n\n#### Args:\n- column (str): The column name.\n- geom_types_list (str): List of shapely geometry types to match against. e.g: Point, Polygon, LineString, MultiPoint, MultiPolygon, MultiLineString\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry_of_type.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry_of_type.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect values in a column to belong to one of the specified geometry types.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_column_values_geometry_to_be_within_place": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_to_be_within_place.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "pygeos", "geopy" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@pjdobson" ], "requirements": [ "pygeos", "geopy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryToBeWithinPlace", "snake_name": "expect_column_values_geometry_to_be_within_place", "short_description": "Expect that column values as geometries are within a place that can be returned through geocoding (as a shape).", "docstring": "Expect that column values as geometries are within a place that can be returned through geocoding (as a shape).\n\nexpect_column_values_geometry_to_be_near_shape is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL. Values must be in longitude - latitude format for this method to work.\n\n#### Keyword Args:\n- place (str): The country, place, address, etc. to query. Expect to return a geometry from OpenStreetMaps (Nominatim)\n- column_shape_format (str): Geometry format for 'column'. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n- geocoder (str): Geocoder from GeoPy to use to return the shape. While this is generic, the api is required to be available from GeoPy and must return a geometry.\n- geocoder_config (dict str): arguments to initialize the GeoPy geocoder. e.g. for paid services, an API_key is usually required. See GeoPy for reference.\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\n#### Notes:\n* The user is responsible to transform the column to a WKT or WKB format that is in the WGS84 coordianate system for earth.\n* Other Coordinate Reference Systems are not yet supported.\n* See Nominatim for appropriate use of the service as open-source. Requests are subject to timeout and blocking.\n* Frequent queries should be cached locally and use (expect_column_value_geometries_to_be_within_shape)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry.within_place.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry.within_place.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometries are within a place that can be returned through geocoding (as a shape).\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_geometry_to_be_within_shape": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_to_be_within_shape.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "pygeos" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@pjdobson" ], "requirements": [ "pygeos" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryToBeWithinShape", "snake_name": "expect_column_values_geometry_to_be_within_shape", "short_description": "Expect that column values as geometries are within a given reference shape.", "docstring": "Expect that column values as geometries are within a given reference shape.\n\nexpect_column_values_geometry_to_be_within_shape is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n\n#### Keyword Args:\n- shape (str or list of str): The reference geometry\n- shape_format (str): Geometry format for 'shape' string(s). Can be provided as 'Well Known Text' (WKT), 'Well Known Binary' (WKB), or as GeoJSON. Must be one of: [wkt, wkb, geojson]. Default: wkt\n- column_shape_format (str): Geometry format for 'column'. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n- properly (boolean): Whether the 'column' values should be properly within in the reference 'shape'. The method allows for shapes to be 'properly contained' within the reference, meaning no points of a given geometry can touch the boundary of the reference. See the pygeos docs for reference. Default: False\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\n#### Notes:\n* Convention is (X Y Z) for points, which would map to (Longitude Latitude Elevation) for geospatial cases.\n* Any convention can be followed as long as the test and reference shapes are consistent.\n* The reference shape allows for an array, but will union (merge) all the shapes into 1 and check the contains condition.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry.within_shape.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry.within_shape.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometries are within a given reference shape.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_column_values_geometry_to_intersect_shape": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_to_intersect_shape.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "pygeos" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-22" ], "contributors": [ "@pjdobson" ], "requirements": [ "pygeos" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryToIntersectShape", "snake_name": "expect_column_values_geometry_to_intersect_shape", "short_description": "Expect that column values as geometries intersect a given reference shape.", "docstring": "Expect that column values as geometries intersect a given reference shape.\n\nexpect_column_values_geometry_to_intersect_shape is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats. WKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n\n#### Keyword Args:\n- shape (str or list of str): The reference geometry\n- shape_format (str): Geometry format for 'shape' string(s). Can be provided as 'Well Known Text' (WKT), 'Well Known Binary' (WKB), or as GeoJSON. Must be one of: [wkt, wkb, geojson]. Default: wkt\n- column_shape_format (str): Geometry format for 'column'. Column values must be provided in WKT or WKB format, which are commom formats for GIS Database formats.\nWKT can be accessed thhrough the ST_AsText() or ST_AsBinary() functions in queries for PostGIS and MSSQL.\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\n#### Notes:\n* Convention is (X Y Z) for points, which would map to (Longitude Latitude Elevation) for geospatial cases.\n* Any convention can be followed as long as the test and reference shapes are consistent.\n* The reference shape allows for an array, but will union (merge) all the shapes into 1 and check the contains condition.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.geometry.intersects_shape.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.geometry.intersects_shape.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that column values as geometries intersect a given reference shape.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 8.0 }, "expect_column_values_geometry_to_overlap": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_geometry_to_overlap.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "rtree", "geopandas", "shapely", "numpy", "rtree", "geopandas", "shapely", "numpy" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "hackathon-22", "geospatial" ], "contributors": [ "@luismdiaz01", "@derekma73" ], "requirements": [ "rtree", "geopandas", "shapely", "numpy" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesGeometryToOverlap", "snake_name": "expect_column_values_geometry_to_overlap", "short_description": "Expect geometries in this column to overlap with each other. If any two geometries do overlap, expectation will return True.", "docstring": "Expect geometries in this column to overlap with each other. If any two geometries do overlap, expectation will return True.\n\nFor more information look here https://stackoverflow.com/questions/64042379/shapely-is-valid-returns-true-to-invalid-overlap-polygons\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.geometry_overlap", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect geometries in this column to overlap with each other. If any two geometries do overlap, expectation will return True.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_lat_lon_to_be_land_or_ocean": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_lat_lon_to_be_land_or_ocean.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "global-land-mask" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@mmi333" ], "requirements": [ "global-land-mask" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesLatLonToBeLandOrOcean", "snake_name": "expect_column_values_lat_lon_to_be_land_or_ocean", "short_description": "Expect values in a column to be lat lon pairs that represent a point on land or in an ocean.", "docstring": "Expect values in a column to be lat lon pairs that represent a point on land or in an ocean.\n\n#### Args:\n- column (str): The column name.\n\nland_or_ocean (str): Either 'land' or 'ocean'. represents whether to check if each point is on land or in an ocean.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.lat_lon_land_or_ocean.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.lat_lon_land_or_ocean.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect values in a column to be lat lon pairs that represent a point on land or in an ocean.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_column_values_point_within_geo_region": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_point_within_geo_region.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "geopandas", "shapely" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "experimental" ], "contributors": [ "@DXcarlos", "@Rxmeez", "@ryanlindeborg", "@mmi333" ], "requirements": [ "geopandas", "shapely" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesPointWithinGeoRegion", "snake_name": "expect_column_values_point_within_geo_region", "short_description": "Expect column values to be lat/lon points within a geographic region.", "docstring": "Expect column values to be lat/lon points within a geographic region.\n\nThis expectation will check a (longitude, latitude) tuple to see if it falls within a country input by the user or a polygon specified by user input points. To do this geo calculation, it leverages the Geopandas library. So for now it only supports the countries that are in the Geopandas world database. Importantly, countries are defined by their iso_a3 country code, not their full name.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.point_within_geo_region.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.point_within_geo_region.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column values to be lat/lon points within a geographic region.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 7.0 }, "expect_column_values_reverse_geocoded_lat_lon_to_contain": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_reverse_geocoded_lat_lon_to_contain.py", "package": "great_expectations_geospatial_expectations", "requirements": [ "geopandas" ], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [ "geospatial", "hackathon-2022" ], "contributors": [ "@mmi333" ], "requirements": [ "geopandas" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesReverseGeocodedLatLonToContain", "snake_name": "expect_column_values_reverse_geocoded_lat_lon_to_contain", "short_description": "Expect values in a column to be tuples of degree-decimal (latitude, longitude) which contain a specific word when reverse geocoded.", "docstring": "Expect values in a column to be tuples of degree-decimal (latitude, longitude) which contain a specific word when reverse geocoded.\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- word (str) : The word to check if it's contained in the reverse geocoded string. Must be a lowercase string.\n\nprovider (str or geopy.geocoder): The reverse geocoding service provider. Default: photon. More info here: https://geopandas.org/en/stable/docs/reference/api/geopandas.tools.reverse_geocode.html\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.reverse_geocoded_lat_lon_contain.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.reverse_geocoded_lat_lon_contain.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect values in a column to be tuples of degree-decimal (latitude, longitude) which contain a specific word when reverse geocoded.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 4.0 }, "expect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point": { "updated_at": "2023-05-22 11:08:40 -0400 2 months ago", "created_at": "2022-09-30 12:33:05 -0600 10 months ago", "path": "contrib/great_expectations_geospatial_expectations/great_expectations_geospatial_expectations/expectations/expect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point.py", "package": "great_expectations_geospatial_expectations", "requirements": [], "import_module_args": [ "great_expectations_geospatial_expectations.expectations" ], "sys_path": "../../contrib/great_expectations_geospatial_expectations", "exp_type": "Contrib ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "geospatial", "hackathon-2022", "distance projection" ], "contributors": [ "@austiezr", "@mmi333" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeLatLonCoordinatesInRangeOfGivenPoint", "snake_name": "expect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point", "short_description": "Expect values in a column to be tuples of degree-decimal (latitude, longitude) within a specified range of a given degree-decimal (latitude, longitude) point.", "docstring": "Expect values in a column to be tuples of degree-decimal (latitude, longitude) within a specified range of a given degree-decimal (latitude, longitude) point.\n\nexpect_column_values_to_be_lat_lon_coordinates_in_range_of_given_point is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- center_point (tuple(float, float) or list(float, float)): The point from which to measure to the column points. Must be a tuple or list of exactly two (2) floats.\n- unit (str or None): The unit of distance with which to measure. Must be one of: [miles, kilometers]. Default: kilometers\n\nrange (int or float): The range in [miles, kilometers] from your specified center_point to measure.\n\nprojection (str or None): The method by which to calculate distance between points. Must be one of: [fcc, pythagorean]. Default: pythagorean\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. 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"contrib/time_series_expectations/time_series_expectations/expectations/expect_batch_row_count_to_match_prophet_date_model.py", "package": "time_series_expectations", "requirements": [ "prophet" ], "import_module_args": [ "time_series_expectations.expectations" ], "sys_path": "../../contrib/time_series_expectations", "exp_type": "Contrib BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [], "contributors": [ "@abegong" ], "requirements": [ "prophet" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectBatchRowCountToMatchProphetDateModel", "snake_name": "expect_batch_row_count_to_match_prophet_date_model", "short_description": "Expect the number of rows in a batch to match the predictions of a prophet model for a given date.", "docstring": "Expect the number of rows in a batch to match the predictions of a prophet model for a given date.\n\nexpect_batch_row_count_to_match_prophet_date_model is a [BatchExpectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_table_expectations)\n\n#### Args:\n\n#### date (str):\nA string representing the date to compare the batch row count to\n\n#### model_json (str):\nA string containing a JSON-serialized Prophet model\n\n#### Keyword Args:\n\n#### Other Parameters:\n\n#### result_format (str or None):\n- Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY.\n- For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n\n#### include_config (boolean):\nIf True, then include the expectation config as part of the result object.\n\n#### catch_exceptions (boolean or None):\nIf True, then catch exceptions and include them as part of the result object.\n- For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n\n#### meta (dict or None):\nA JSON-serializable dictionary (nesting allowed) that will be included in the output without\n- modification. 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For more information, please see the [project github page](https://github.com/facebook/prophet).\n\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.row_count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of rows in a batch to match the predictions of a prophet model for a given date.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_column_max_to_match_prophet_date_model": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-02-24 20:40:35 -0700 5 months ago", "path": "contrib/time_series_expectations/time_series_expectations/expectations/expect_column_max_to_match_prophet_date_model.py", "package": "time_series_expectations", "requirements": [ "prophet" ], "import_module_args": [ "time_series_expectations.expectations" ], "sys_path": "../../contrib/time_series_expectations", "exp_type": "Contrib ColumnAggregateTimeSeriesExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [], "contributors": [ "@abegong" ], "requirements": [ "prophet" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnMaxToMatchProphetDateModel", "snake_name": "expect_column_max_to_match_prophet_date_model", "short_description": "Expect the column maximum to match the predictions of a prophet model for a given date.", "docstring": "Expect the column maximum to match the predictions of a prophet model for a given date.\n\nexpect_column_max_to_match_prophet_date_model is a ColumnAggregateTimeSeriesExpectation.\n\n#### Args:\n\n#### column (str):\nThe name of the column to calculate the max of\n\n#### date (str):\nA string representing the date to compare the max to\n\n#### model_json (str):\nA string containing a JSON-serialized Prophet model\n\n#### Keyword Args:\n\n#### Other Parameters:\n\n#### result_format (str or None):\n- Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY.\n- For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n\n#### include_config (boolean):\nIf True, then include the expectation config as part of the result object.\n\n#### catch_exceptions (boolean or None):\nIf True, then catch exceptions and include them as part of the result object.\n- For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n\n#### meta (dict or None):\nA JSON-serializable dictionary (nesting allowed) that will be included in the output without\n- modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n- * Prophet is an open source forecasting library created at facebook. For more information, please see the [project github page](https://github.com/facebook/prophet).\n* I apologize for the dangling prepositions in the Arg docstrings for this Expectation.\n\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.max", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": false, "doc_url": null, "sub_messages": [ { "message": "No validate_configuration method defined on subclass", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column maximum to match the predictions of a prophet model for a given date.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_column_pair_values_to_match_prophet_date_model": { "updated_at": "2023-07-20 13:06:00 -0500 13 days ago", "created_at": "2023-02-24 20:40:35 -0700 5 months ago", "path": "contrib/time_series_expectations/time_series_expectations/expectations/expect_column_pair_values_to_match_prophet_date_model.py", "package": "time_series_expectations", "requirements": [ "prophet" ], "import_module_args": [ "time_series_expectations.expectations" ], "sys_path": "../../contrib/time_series_expectations", "exp_type": "Contrib ColumnPairMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "BETA", "tags": [], "contributors": [ "@abegong" ], "requirements": [ "prophet" ], "library_metadata_passed_checks": true, "has_full_test_suite": false, "manually_reviewed_code": false, "problems": [] }, "description": { "camel_name": "ExpectColumnPairValuesToMatchProphetDateModel", "snake_name": "expect_column_pair_values_to_match_prophet_date_model", "short_description": "Expect the values in column A to match the predictions of a prophet model based on the timestamp in column B.", "docstring": "Expect the values in column A to match the predictions of a prophet model based on the timestamp in column B.\n\nexpect_column_pair_values_to_match_prophet_date_model is a [ColumnPairMapExpectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_pair_map_expectations)\n\n#### Args:\n\n#### column_A (str):\nThe name of the timestamp column\n\n#### column_B (str):\nThe name of the column containing time series values\n\n#### model_json (str):\nA string containing a JSON-serialized Prophet model\n\n#### Keyword Args:\n\n#### mostly (None or a float between 0 and 1):\nSuccessful if at least mostly fraction of values match the expectation.\n- For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n\n#### result_format (str or None):\n- Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY.\n- For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n\n#### include_config (boolean):\nIf True, then include the expectation config as part of the result object.\n\n#### catch_exceptions (boolean or None):\nIf True, then catch exceptions and include them as part of the result object.\n- For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n\n#### meta (dict or None):\nA JSON-serializable dictionary (nesting allowed) that will be included in the output without\n- modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n- * Prophet is an open source forecasting library created at facebook. For more information, please see the [project github page](https://github.com/facebook/prophet).\n\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_pair_values.match_prophet_forecast.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_pair_values.match_prophet_forecast.filtered_row_count", "has_question_renderer": false }, { "name": "column_pair_values.match_prophet_forecast.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": false, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": false, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for spark are passing", "passed": true }, { "message": "All 3 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the values in column A to match the predictions of a prophet model based on the timestamp in column B.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for spark are passing", "passed": true }, { "message": "All 3 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 10.0 }, "expect_column_distinct_values_to_be_in_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_distinct_values_to_be_in_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnDistinctValuesToBeInSet", "snake_name": "expect_column_distinct_values_to_be_in_set", "short_description": "Expect the set of distinct column values to be contained by a given set.", "docstring": "Expect the set of distinct column values to be contained by a given set.\n\nexpect_column_distinct_values_to_be_in_set is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\nThe success value for this expectation will match that of [expect_column_values_to_be_in_set](https://greatexpectations.io/expectations/expect_column_values_to_be_in_set).\n\n#### For example:\n```\n# my_df.my_col = [1,2,2,3,3,3]\n>>> my_df.expect_column_distinct_values_to_be_in_set(\n \"my_col\",\n [2, 3, 4]\n )\n{\n \"success\": false\n \"result\": {\n \"observed_value\": [1,2,3],\n \"details\": {\n \"value_counts\": [\n {\n \"value\": 1,\n \"count\": 1\n },\n {\n \"value\": 2,\n \"count\": 1\n },\n {\n \"value\": 3,\n \"count\": 1\n }\n ]\n }\n }\n}\n```\n\n#### Args:\n- column (str): The column name.\n- value_set (set-like): A set of objects used for comparison.\n\n#### Keyword Args:\n- parse_strings_as_datetimes (boolean or None): If True values provided in value_set will be parsed as datetimes before making comparisons.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_distinct_values_to_contain_set](https://greatexpectations.io/expectations/expect_column_distinct_values_to_contain_set)\n- [expect_column_distinct_values_to_equal_set](https://greatexpectations.io/expectations/expect_column_distinct_values_to_equal_set)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.value_counts", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 10, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the set of distinct column values to be contained by a given set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 113.0 }, "expect_column_distinct_values_to_contain_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_distinct_values_to_contain_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnDistinctValuesToContainSet", "snake_name": "expect_column_distinct_values_to_contain_set", "short_description": "Expect the set of distinct column values to contain a given set.", "docstring": "Expect the set of distinct column values to contain a given set.\n\nexpect_column_distinct_values_to_contain_set is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- value_set (set-like): A set of objects used for comparison.\n\n#### Keyword Args:\n- parse_strings_as_datetimes (boolean or None): If True values provided in value_set will be parsed as datetimes before making comparisons.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_distinct_values_to_be_in_set](https://greatexpectations.io/expectations/expect_column_distinct_values_to_be_in_set)\n- [expect_column_distinct_values_to_equal_set](https://greatexpectations.io/expectations/expect_column_distinct_values_to_equal_set)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.value_counts", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 15, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 15 tests for snowflake are passing", "passed": true }, { "message": "All 15 tests for trino are passing", "passed": true }, { "message": "All 15 tests for redshift are passing", "passed": true }, { "message": "All 15 tests for postgresql are passing", "passed": true }, { "message": "All 15 tests for bigquery are passing", "passed": true }, { "message": "All 15 tests for mssql are passing", "passed": true }, { "message": "All 15 tests for spark are passing", "passed": true }, { "message": "All 15 tests for pandas are passing", "passed": true }, { "message": "All 11 tests for sqlite are passing", "passed": true }, { "message": "All 15 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the set of distinct column values to contain a given set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 15 tests for snowflake are passing", "passed": true }, { "message": "All 15 tests for trino are passing", "passed": true }, { "message": "All 15 tests for redshift are passing", "passed": true }, { "message": "All 15 tests for postgresql are passing", "passed": true }, { "message": "All 15 tests for bigquery are passing", "passed": true }, { "message": "All 15 tests for mssql are passing", "passed": true }, { "message": "All 15 tests for spark are passing", "passed": true }, { "message": "All 15 tests for pandas are passing", "passed": true }, { "message": "All 11 tests for sqlite are passing", "passed": true }, { "message": "All 15 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 159.0 }, "expect_column_distinct_values_to_equal_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_distinct_values_to_equal_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnDistinctValuesToEqualSet", "snake_name": "expect_column_distinct_values_to_equal_set", "short_description": "Expect the set of distinct column values to equal a given set.", "docstring": "Expect the set of distinct column values to equal a given set.\n\nexpect_column_distinct_values_to_equal_set is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- value_set (set-like): A set of objects used for comparison.\n\n#### Keyword Args:\n- parse_strings_as_datetimes (boolean or None): If True values provided in value_set will be parsed as datetimes before making comparisons.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_distinct_values_to_be_in_set](https://greatexpectations.io/expectations/expect_column_distinct_values_to_be_in_set)\n- [expect_column_distinct_values_to_contain_set](https://greatexpectations.io/expectations/expect_column_distinct_values_to_contain_set)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.value_counts", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], 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null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the set of distinct column values to equal a given set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 103.0 }, "expect_column_kl_divergence_to_be_less_than": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_kl_divergence_to_be_less_than.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation", "distributional expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnKlDivergenceToBeLessThan", "snake_name": "expect_column_kl_divergence_to_be_less_than", "short_description": "Expect the Kulback-Leibler (KL) divergence (relative entropy) of the specified column with respect to the partition object to be lower than the provided threshold.", "docstring": "Expect the Kulback-Leibler (KL) divergence (relative entropy) of the specified column with respect to the partition object to be lower than the provided threshold.\n\nKL divergence compares two distributions. The higher the divergence value (relative entropy), the larger the difference between the two distributions. A relative entropy of zero indicates that the data are distributed identically, when binned according to the provided partition.\n\nIn many practical contexts, choosing a value between 0.5 and 1 will provide a useful test.\n\nThis expectation works on both categorical and continuous partitions. See notes below for details.\n\nexpect_column_kl_divergence_to_be_less_than is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- partition_object (dict): The expected partition object (see [partition_object](https://docs.greatexpectations.io/docs/reference/expectations/distributional_expectations/#partition-objects)).\n- threshold (float): The maximum KL divergence to for which to return success=True. If KL divergence is larger than the provided threshold, the test will return success=False.\n\n#### Keyword Args:\n- internal_weight_holdout (float between 0 and 1 or None): The amount of weight to split uniformly among zero-weighted partition bins. internal_weight_holdout provides a mechanisms to make the test less strict by assigning positive weights to values observed in the data for which the partition explicitly expected zero weight. With no internal_weight_holdout, any value observed in such a region will cause KL divergence to rise to +Infinity. Defaults to 0.\n- tail_weight_holdout (float between 0 and 1 or None): The amount of weight to add to the tails of the histogram. Tail weight holdout is split evenly between (-Infinity, min(partition_object['bins'])) and (max(partition_object['bins']), +Infinity). tail_weight_holdout provides a mechanism to make the test less strict by assigning positive weights to values observed in the data that are not present in the partition. With no tail_weight_holdout, any value observed outside the provided partition_object will cause KL divergence to rise to +Infinity. Defaults to 0.\n- bucketize_data (boolean): If True, then continuous data will be bucketized before evaluation. Setting this parameter to false allows evaluation of KL divergence with a None partition object for profiling against discrete data.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* observed_value field in the result object is customized for this expectation to be a float representing the true KL divergence (relative entropy) or None if the value is calculated as infinity, -infinity, or NaN\n* details.observed_partition in the result object is customized for this expectation to be a dict representing the partition observed in the data\n* details.expected_partition in the result object is customized for this expectation to be a dict representing the partition against which the data were compared, after applying specified weight holdouts\n\nIf the partition_object is categorical, this expectation will expect the values in column to also be categorical.\n\n* If the column includes values that are not present in the partition, the tail_weight_holdout will be equally split among those values, providing a mechanism to weaken the strictness of the expectation (otherwise, relative entropy would immediately go to infinity).\n* If the partition includes values that are not present in the column, the test will simply include zero weight for that value.\n\nIf the partition_object is continuous, this expectation will discretize the values in the column according to the bins specified in the partition_object, and apply the test to the resulting distribution.\n\n* The internal_weight_holdout and tail_weight_holdout parameters provide a mechanism to weaken the expectation, since an expected weight of zero would drive relative entropy to be infinite if any data are observed in that interval.\n* If internal_weight_holdout is specified, that value will be distributed equally among any intervals with weight zero in the partition_object.\n* If tail_weight_holdout is specified, that value will be appended to the tails of the bins ((-Infinity, min(bins)) and (max(bins), Infinity).\n\nIf relative entropy/kl divergence goes to infinity for any of the reasons mentioned above, the observed value will be set to None. This is because inf, -inf, Nan, are not json serializable and cause some json parsers to crash when encountered. The python None token will be serialized to null in json.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.value_counts", "has_question_renderer": false }, { "name": "column_values.nonnull.count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 25, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 45, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 44, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 45, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, 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"sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the Kulback-Leibler (KL) divergence (relative entropy) of the specified column with respect to the partition object to be lower than the provided threshold.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 45 tests for snowflake are passing", "passed": true }, { "message": "All 45 tests for trino are passing", "passed": true }, { "message": "All 45 tests for redshift are passing", "passed": true }, { "message": "All 45 tests for postgresql are passing", "passed": true }, { "message": "All 25 tests for bigquery are passing", "passed": true }, { "message": "All 45 tests for mssql are passing", "passed": true }, { "message": "All 45 tests for spark are passing", "passed": true }, { "message": "All 45 tests for pandas are passing", "passed": true }, { "message": "All 44 tests for sqlite are passing", "passed": true }, { "message": "All 45 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 442.0 }, "expect_column_max_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_max_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnMaxToBeBetween", "snake_name": "expect_column_max_to_be_between", "short_description": "Expect the column maximum to be between a minimum value and a maximum value.", "docstring": "Expect the column maximum to be between a minimum value and a maximum value.\n\nexpect_column_max_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations)\n\n#### Args:\n- column (str): The column name\n- min_value (comparable type or None): The minimum value of the acceptable range for the column maximum.\n- max_value (comparable type or None): The maximum value of the acceptable range for the column maximum.\n- strict_min (boolean): If True, the lower bound of the column maximum acceptable range must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the upper bound of the column maximum acceptable range must be strictly smaller than max_value, default=False\n\n#### Keyword Args:\n- parse_strings_as_datetimes (Boolean or None): If True, parse min_value, max_values, and all non-null column values to datetimes before making comparisons.\n- output_strftime_format (str or None): A valid strfime format for datetime output. Only used if parse_strings_as_datetimes=True.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be a list representing the actual column max\n\n#### See Also:\n- [expect_column_min_to_be_between](https://greatexpectations.io/expectations/expect_column_min_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.max", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column maximum to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 93.0 }, "expect_column_mean_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_mean_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnMeanToBeBetween", "snake_name": "expect_column_mean_to_be_between", "short_description": "Expect the column mean to be between a minimum value and a maximum value (inclusive).", "docstring": "Expect the column mean to be between a minimum value and a maximum value (inclusive).\n\nexpect_column_mean_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- min_value (float or None): The minimum value for the column mean.\n- max_value (float or None): The maximum value for the column mean.\n- strict_min (boolean): If True, the column mean must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the column mean must be strictly smaller than max_value, default=False\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound.\n* If max_value is None, then min_value is treated as a lower bound.\n* observed_value field in the result object is customized for this expectation to be a float representing the true mean for the column\n\n#### See Also:\n- [expect_column_median_to_be_between](https://greatexpectations.io/expectations/expect_column_median_to_be_between)\n- [expect_column_stdev_to_be_between](https://greatexpectations.io/expectations/expect_column_stdev_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.mean", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 13, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 14, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 13, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 13, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 11 tests for snowflake are passing", "passed": true }, { "message": "All 11 tests for trino are passing", "passed": true }, { "message": "All 11 tests for redshift are passing", "passed": true }, { "message": "All 11 tests for postgresql are passing", "passed": true }, { "message": "All 11 tests for bigquery are passing", "passed": true }, { "message": "All 11 tests for mssql are passing", "passed": true }, { "message": "All 13 tests for spark are passing", "passed": true }, { "message": "All 14 tests for pandas are passing", "passed": true }, { "message": "All 13 tests for sqlite are passing", "passed": true }, { "message": "All 13 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column mean to be between a minimum value and a maximum value (inclusive).\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 11 tests for snowflake are passing", "passed": true }, { "message": "All 11 tests for trino are passing", "passed": true }, { "message": "All 11 tests for redshift are passing", "passed": true }, { "message": "All 11 tests for postgresql are passing", "passed": true }, { "message": "All 11 tests for bigquery are passing", "passed": true }, { "message": "All 11 tests for mssql are passing", "passed": true }, { "message": "All 13 tests for spark are passing", "passed": true }, { "message": "All 14 tests for pandas are passing", "passed": true }, { "message": "All 13 tests for sqlite are passing", "passed": true }, { "message": "All 13 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 132.0 }, "expect_column_median_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_median_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnMedianToBeBetween", "snake_name": "expect_column_median_to_be_between", "short_description": "Expect the column median to be between a minimum value and a maximum value.", "docstring": "Expect the column median to be between a minimum value and a maximum value.\n\nexpect_column_median_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- min_value (int or None): The minimum value for the column median.\n- max_value (int or None): The maximum value for the column median.\n- strict_min (boolean): If True, the column median must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the column median must be strictly smaller than max_value, default=False\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be a float representing the true median for the column\n\n#### See Also:\n- [expect_column_mean_to_be_between](https://greatexpectations.io/expectations/expect_column_mean_to_be_between)\n- [expect_column_stdev_to_be_between](https://greatexpectations.io/expectations/expect_column_stdev_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.median", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 10, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column median to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 112.0 }, "expect_column_min_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_min_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnMinToBeBetween", "snake_name": "expect_column_min_to_be_between", "short_description": "Expect the column minimum to be between a minimum value and a maximum value.", "docstring": "Expect the column minimum to be between a minimum value and a maximum value.\n\nexpect_column_min_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name\n- min_value (comparable type or None): The minimal column minimum allowed.\n- max_value (comparable type or None): The maximal column minimum allowed.\n- strict_min (boolean): If True, the minimal column minimum must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the maximal column minimum must be strictly smaller than max_value, default=False\n\n#### Keyword Args:\n- parse_strings_as_datetimes (Boolean or None): If True, parse min_value, max_values, and all non-null column values to datetimes before making comparisons.\n- output_strftime_format (str or None): A valid strfime format for datetime output. Only used if parse_strings_as_datetimes=True.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be a list representing the actual column min\n\n#### See Also:\n- [expect_column_max_to_be_between](https://greatexpectations.io/expectations/expect_column_max_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.min", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column minimum to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 93.0 }, "expect_column_most_common_value_to_be_in_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_most_common_value_to_be_in_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnMostCommonValueToBeInSet", "snake_name": "expect_column_most_common_value_to_be_in_set", "short_description": "Expect the most common value to be within the designated value set.", "docstring": "Expect the most common value to be within the designated value set.\n\nexpect_column_most_common_value_to_be_in_set is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name\n- value_set (set-like): A list of potential values to match\n\n#### Keyword Args:\n- ties_okay (boolean or None): If True, then the expectation will still succeed if values outside the designated set are as common (but not more common) than designated values\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* observed_value field in the result object is customized for this expectation to be a list representing the most common values in the column, which is often a single element... if there is a tie for most common among multiple values, observed_value will contain a single copy of each most common value\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.most_common_value", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the most common value to be within the designated value set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 93.0 }, "expect_column_pair_values_a_to_be_greater_than_b": { "updated_at": "2023-06-23 12:46:28 -0700 6 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_pair_values_a_to_be_greater_than_b.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnPairMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column pair map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnPairValuesAToBeGreaterThanB", "snake_name": "expect_column_pair_values_a_to_be_greater_than_b", "short_description": "Expect the values in column A to be greater than column B.", "docstring": "Expect the values in column A to be greater than column B.\n\nexpect_column_pair_values_a_to_be_greater_than_b is a [Column Pair Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_pair_map_expectations).\n\n#### Args:\n- column_A (str): The first column name\n- column_B (str): The second column name\n- or_equal (boolean or None): If True, then values can be equal, not strictly greater\n\n#### Keyword Args:\n- ignore_row_if (str): \"both_values_are_missing\", \"either_value_is_missing\", \"neither\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_pair_values.a_greater_than_b.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_pair_values.a_greater_than_b.filtered_row_count", "has_question_renderer": false }, { "name": "column_pair_values.a_greater_than_b.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 0, "num_failed": 1, "failing_names": [ "all" ] }, { "backend": "postgresql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true }, { "message": "Only 0 / 1 tests for redshift are passing", "passed": false }, { "message": " - Failing: all", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the values in column A to be greater than column B.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 57.5 }, "expect_column_pair_values_to_be_equal": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_pair_values_to_be_equal.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnPairMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column pair map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnPairValuesToBeEqual", "snake_name": "expect_column_pair_values_to_be_equal", "short_description": "Expect the values in column A to be the same as column B.", "docstring": "Expect the values in column A to be the same as column B.\n\nexpect_column_pair_values_to_be_equal is a [Column Pair Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_pair_map_expectations).\n\n#### Args:\n- column_A (str): The first column name\n- column_B (str): The second column name\n\n#### Keyword Args:\n- ignore_row_if (str): \"both_values_are_missing\", \"either_value_is_missing\", \"neither\"\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_pair_values.equal.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_pair_values.equal.filtered_row_count", "has_question_renderer": false }, { "name": "column_pair_values.equal.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 9, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 12 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the values in column A to be the same as column B.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 12 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 106.0 }, "expect_column_pair_values_to_be_in_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_pair_values_to_be_in_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnPairMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column pair map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnPairValuesToBeInSet", "snake_name": "expect_column_pair_values_to_be_in_set", "short_description": "Expect the paired values from columns A and B to belong to a set of valid pairs.", "docstring": "Expect the paired values from columns A and B to belong to a set of valid pairs.\n\nexpect_column_pair_values_to_be_in_set is a [Column Pair Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_pair_map_expectations).\n\n#### For example:\n```\n>>> d = {'fruit': ['appple','apple','apple','banana','banana'],\n 'color': ['red','green','yellow','yellow','red']}\n>>> my_df = pd.DataFrame(data=d)\n>>> my_df.expect_column_pair_values_to_be_in_set(\n 'fruit',\n 'color',\n [\n ('apple','red'),\n ('apple','green'),\n ('apple','yellow'),\n ('banana','yellow'),\n ]\n)\n{\n \"success\": false,\n \"meta\": {},\n \"exception_info\": {\n \"raised_exception\": false,\n \"exception_traceback\": null,\n \"exception_message\": null\n },\n \"result\": {\n \"element_count\": 5,\n \"unexpected_count\": 1,\n \"unexpected_percent\": 20.0,\n \"partial_unexpected_list\": [\n [\n \"banana\",\n \"red\"\n ]\n ],\n \"missing_count\": 0,\n \"missing_percent\": 0.0,\n \"unexpected_percent_total\": 20.0,\n \"unexpected_percent_nonmissing\": 20.0\n }\n}\n```\n\n#### Args:\n- column_A (str): The first column name\n- column_B (str): The second column name\n- value_pairs_set (list of tuples): All the valid pairs to be matched\n\n#### Keyword Args:\n- ignore_row_if (str): \"both_values_are_missing\", \"either_value_is_missing\", \"neither\"\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_pair_values.in_set.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_pair_values.in_set.filtered_row_count", "has_question_renderer": false }, { "name": "column_pair_values.in_set.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the paired values from columns A and B to belong to a set of valid pairs.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 53.0 }, "expect_column_proportion_of_unique_values_to_be_between": { "updated_at": "2023-07-24 12:43:31 -0400 9 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_proportion_of_unique_values_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnProportionOfUniqueValuesToBeBetween", "snake_name": "expect_column_proportion_of_unique_values_to_be_between", "short_description": "Expect the proportion of unique values to be between a minimum value and a maximum value.", "docstring": "Expect the proportion of unique values to be between a minimum value and a maximum value.\n\nFor example, in a column containing [1, 2, 2, 3, 3, 3, 4, 4, 4, 4], there are 4 unique values and 10 total values for a proportion of 0.4.\n\nexpect_column_proportion_of_unique_values_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- min_value (float or None): The minimum proportion of unique values. (Proportions are on the range 0 to 1)\n- max_value (float or None): The maximum proportion of unique values. (Proportions are on the range 0 to 1)\n- strict_min (boolean): If True, the minimum proportion of unique values must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the maximum proportion of unique values must be strictly smaller than max_value, default=False\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be a float representing the proportion of unique values in the column\n\n#### See Also:\n- [expect_column_unique_value_count_to_be_between](https://greatexpectations.io/expectations/expect_column_unique_value_count_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.unique_proportion", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the proportion of unique values to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 63.0 }, "expect_column_quantile_values_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_quantile_values_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnQuantileValuesToBeBetween", "snake_name": "expect_column_quantile_values_to_be_between", "short_description": "Expect the specific provided column quantiles to be between a minimum value and a maximum value.", "docstring": "Expect the specific provided column quantiles to be between a minimum value and a maximum value.\n\nexpect_column_quantile_values_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### For example:\n```\n# my_df.my_col = [1,2,2,3,3,3,4]\n>>> my_df.expect_column_quantile_values_to_be_between(\n \"my_col\",\n {\n \"quantiles\": [0., 0.333, 0.6667, 1.],\n \"value_ranges\": [[0,1], [2,3], [3,4], [4,5]]\n }\n)\n{\n \"success\": True,\n \"result\": {\n \"observed_value\": {\n \"quantiles: [0., 0.333, 0.6667, 1.],\n \"values\": [1, 2, 3, 4],\n }\n \"element_count\": 7,\n \"missing_count\": 0,\n \"missing_percent\": 0.0,\n \"details\": {\n \"success_details\": [true, true, true, true]\n }\n }\n }\n}\n```\n\nexpect_column_quantile_values_to_be_between can be computationally intensive for large datasets.\n\n#### Args:\n- column (str): The column name.\n- quantile_ranges (dictionary with keys 'quantiles' and 'value_ranges'): Key 'quantiles' is an increasingly ordered list of desired quantile values (floats). Key 'value_ranges' is a list of 2-value lists that specify a lower and upper bound (inclusive) for the corresponding quantile (with [min, max] ordering). The length of the 'quantiles' list and the 'value_ranges' list must be equal.\n- allow_relative_error (boolean or string): Whether to allow relative error in quantile communications on backends that support or require it.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive.\n* If min_value is None, then max_value is treated as an upper bound only\n* If max_value is None, then min_value is treated as a lower bound only\n* details.success_details field in the result object is customized for this expectation\n\n#### See Also:\n- [expect_column_min_to_be_between](https://greatexpectations.io/expectations/expect_column_min_to_be_between)\n- [expect_column_max_to_be_between](https://greatexpectations.io/expectations/expect_column_max_to_be_between)\n- [expect_column_median_to_be_between](https://greatexpectations.io/expectations/expect_column_median_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.quantile_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL 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"message": "\"Expect the specific provided column quantiles to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 56.0 }, "expect_column_stdev_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_stdev_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnStdevToBeBetween", "snake_name": "expect_column_stdev_to_be_between", "short_description": "Expect the column standard deviation to be between a minimum value and a maximum value.", "docstring": "Expect the column standard deviation to be between a minimum value and a maximum value.\n\nUses sample standard deviation (normalized by N-1).\n\nexpect_column_stdev_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- min_value (float or None): The minimum value for the column standard deviation.\n- max_value (float or None): The maximum value for the column standard deviation.\n- strict_min (boolean): If True, the column standard deviation must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the column standard deviation must be strictly smaller than max_value, default=False\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be a float representing the true standard deviation for the column\n\n#### See Also:\n- [expect_column_mean_to_be_between](https://greatexpectations.io/expectations/expect_column_mean_to_be_between)\n- [expect_column_median_to_be_between](https://greatexpectations.io/expectations/expect_column_median_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.standard_deviation", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 10, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column standard deviation to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 110.0 }, "expect_column_sum_to_be_between": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_sum_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnSumToBeBetween", "snake_name": "expect_column_sum_to_be_between", "short_description": "Expect the column to sum to be between a minimum value and a maximum value.", "docstring": "Expect the column to sum to be between a minimum value and a maximum value.\n\nexpect_column_sum_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name\n- min_value (comparable type or None): The minimal sum allowed.\n- max_value (comparable type or None): The maximal sum allowed.\n- strict_min (boolean): If True, the minimal sum must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, the maximal sum must be strictly smaller than max_value, default=False\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be a list representing the actual column sum\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.sum", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 0, "num_failed": 1, "failing_names": [ "all" ] }, { "backend": "mssql", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 7, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 7 tests for snowflake are passing", "passed": true }, { "message": "All 7 tests for trino are passing", "passed": true }, { "message": "All 7 tests for redshift are passing", "passed": true }, { "message": "All 7 tests for postgresql are passing", "passed": true }, { "message": "All 7 tests for mssql are passing", "passed": true }, { "message": "All 7 tests for spark are passing", "passed": true }, { "message": "All 7 tests for pandas are passing", "passed": true }, { "message": "All 7 tests for sqlite are passing", "passed": true }, { "message": "All 7 tests for mysql are passing", "passed": true }, { "message": "Only 0 / 1 tests for bigquery are passing", "passed": false }, { "message": " - Failing: all", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column to sum to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 7 tests for snowflake are passing", "passed": true }, { "message": "All 7 tests for trino are passing", "passed": true }, { "message": "All 7 tests for redshift are passing", "passed": true }, { "message": "All 7 tests for postgresql are passing", "passed": true }, { "message": "All 7 tests for mssql are passing", "passed": true }, { "message": "All 7 tests for spark are passing", "passed": true }, { "message": "All 7 tests for pandas are passing", "passed": true }, { "message": "All 7 tests for sqlite are passing", "passed": true }, { "message": "All 7 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 74.5 }, "expect_column_to_exist": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_to_exist.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnToExist", "snake_name": "expect_column_to_exist", "short_description": "Expect the specified column to exist.", "docstring": "Expect the specified column to exist.\n\nexpect_column_to_exist is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Other Parameters:\n- column_index (int or None): If not None, checks the order of the columns. The expectation will fail if the column is not in location column_index (zero-indexed).\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.columns", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the specified column to exist.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 53.0 }, "expect_column_unique_value_count_to_be_between": { "updated_at": "2023-07-24 12:43:31 -0400 9 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_unique_value_count_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnAggregateExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column aggregate expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnUniqueValueCountToBeBetween", "snake_name": "expect_column_unique_value_count_to_be_between", "short_description": "Expect the number of unique values to be between a minimum value and a maximum value.", "docstring": "Expect the number of unique values to be between a minimum value and a maximum value.\n\nexpect_column_unique_value_count_to_be_between is a [Column Aggregate Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_aggregate_expectations).\n\n#### Args:\n- column (str): The column name.\n- min_value (int or None): The minimum number of unique values allowed.\n- max_value (int or None): The maximum number of unique values allowed.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive.\n* If min_value is None, then max_value is treated as an upper bound\n* If max_value is None, then min_value is treated as a lower bound\n* observed_value field in the result object is customized for this expectation to be an int representing the number of unique values the column\n\n#### See Also:\n- [expect_column_proportion_of_unique_values_to_be_between](https://greatexpectations.io/expectations/expect_column_proportion_of_unique_values_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column.distinct_values.count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of unique values to be between a minimum value and a maximum value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 63.0 }, "expect_column_value_lengths_to_be_between": { "updated_at": "2023-07-24 12:43:31 -0400 9 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_value_lengths_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValueLengthsToBeBetween", "snake_name": "expect_column_value_lengths_to_be_between", "short_description": "Expect the column entries to be strings with length between a minimum value and a maximum value (inclusive).", "docstring": "Expect the column entries to be strings with length between a minimum value and a maximum value (inclusive).\n\nThis expectation only works for string-type values. Invoking it on ints or floats will raise a TypeError.\n\nexpect_column_value_lengths_to_be_between is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- min_value (int or None): The minimum value for a column entry length.\n- max_value (int or None): The maximum value for a column entry length.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive.\n* If min_value is None, then max_value is treated as an upper bound, and the number of acceptable rows has no minimum.\n* If max_value is None, then min_value is treated as a lower bound, and the number of acceptable rows has no maximum.\n\n#### See Also:\n- [expect_column_value_lengths_to_equal](https://greatexpectations.io/expectations/expect_column_value_lengths_to_equal)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": 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}, { "backend": "spark", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 14, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 11, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { 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are passing", "passed": true }, { "message": "All 11 tests for spark are passing", "passed": true }, { "message": "All 14 tests for pandas are passing", "passed": true }, { "message": "All 11 tests for sqlite are passing", "passed": true }, { "message": "All 11 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 126.0 }, "expect_column_value_lengths_to_equal": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_value_lengths_to_equal.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValueLengthsToEqual", "snake_name": "expect_column_value_lengths_to_equal", "short_description": "Expect the column entries to be strings with length equal to the provided value.", "docstring": "Expect the column entries to be strings with length equal to the provided value.\n\nThis expectation only works for string-type values. Invoking it on ints or floats will raise a TypeError.\n\nexpect_column_values_to_be_between is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- value (int or None): The expected value for a column entry length.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. 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"expect_column_value_z_scores_to_be_less_than": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_value_z_scores_to_be_less_than.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValueZScoresToBeLessThan", "snake_name": "expect_column_value_z_scores_to_be_less_than", "short_description": "Expect the Z-scores of a column's values to be less than a given threshold.", "docstring": "Expect the Z-scores of a column's values to be less than a given threshold.\n\nexpect_column_values_to_be_of_type is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations) for typed-column backends, and also for PandasExecutionEngine where the column dtype and provided type_ are unambiguous constraints (any dtype except 'object' or dtype of 'object' with type_ specified as 'object').\n\n#### Args:\n- column (str): The column name of a numerical column.\n- threshold (number): A maximum Z-score threshold. All column Z-scores that are lower than this threshold will evaluate successfully.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n- double_sided (boolean): A True or False value indicating whether to evaluate double sidedly. Examples... (double_sided = True, threshold = 2) -> Z scores in non-inclusive interval(-2,2) | (double_sided = False, threshold = 2) -> Z scores in non-inclusive interval (-infinity,2)\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the Expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. 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"great_expectations/expectations/core/expect_column_values_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeBetween", "snake_name": "expect_column_values_to_be_between", "short_description": "Expect the column entries to be between a minimum value and a maximum value (inclusive).", "docstring": "Expect the column entries to be between a minimum value and a maximum value (inclusive).\n\nexpect_column_values_to_be_between is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations)\n\n#### Args:\n- column (str): The column name.\n- min_value (comparable type or None): The minimum value for a column entry.\n- max_value (comparable type or None): The maximum value for a column entry.\n- strict_min (boolean): If True, values must be strictly larger than min_value, default=False\n- strict_max (boolean): If True, values must be strictly smaller than max_value, default=False\n\n#### Keyword Args:\n- parse_strings_as_datetimes (boolean or None) : If True, parse min_value, max_value, and all non-null column values to datetimes before making comparisons.\n- output_strftime_format (str or None): A valid strfime format for datetime output. Only used if parse_strings_as_datetimes=True.\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive unless strict_min or strict_max are set to True.\n* If min_value is None, then max_value is treated as an upper bound, and there is no minimum value checked.\n* If max_value is None, then min_value is treated as a lower bound, and there is no maximum value checked.\n\n#### See Also:\n- [expect_column_value_lengths_to_be_between](https://greatexpectations.io/expectations/expect_column_value_lengths_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.between.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.between.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 28, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 25, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 29, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 29, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 0, "num_failed": 1, "failing_names": [ "all" ] }, { "backend": "mssql", "num_passed": 29, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 30, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 35, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 29, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 29, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 28 tests for snowflake are passing", "passed": true }, { "message": "All 25 tests for trino are passing", "passed": true }, { "message": "All 29 tests for redshift are passing", "passed": true }, { "message": "All 29 tests for postgresql are passing", "passed": true }, { "message": "All 29 tests for mssql are passing", "passed": true }, { "message": "All 30 tests for spark are passing", "passed": true }, { "message": "All 35 tests for pandas are passing", "passed": true }, { "message": "All 29 tests for sqlite are passing", "passed": true }, { "message": "All 29 tests for mysql are passing", "passed": true }, { "message": "Only 0 / 1 tests for bigquery are passing", "passed": false }, { "message": " - Failing: all", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be between a minimum value and a maximum value (inclusive).\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 28 tests for snowflake are passing", "passed": true }, { "message": "All 25 tests for trino are passing", "passed": true }, { "message": "All 29 tests for redshift are passing", "passed": true }, { "message": "All 29 tests for postgresql are passing", "passed": true }, { "message": "All 29 tests for mssql are passing", "passed": true }, { "message": "All 30 tests for spark are passing", "passed": true }, { "message": "All 35 tests for pandas are passing", "passed": true }, { "message": "All 29 tests for sqlite are passing", "passed": true }, { "message": "All 29 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 274.5 }, "expect_column_values_to_be_dateutil_parseable": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_dateutil_parseable.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeDateutilParseable", "snake_name": "expect_column_values_to_be_dateutil_parseable", "short_description": "Expect the column entries to be parsable using dateutil.", "docstring": "Expect the column entries to be parsable using dateutil.\n\nexpect_column_values_to_be_dateutil_parseable is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations)\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.dateutil_parseable.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.dateutil_parseable.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine" ], "backend_test_result_counts": [ { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be parsable using dateutil.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 6.0 }, "expect_column_values_to_be_decreasing": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_decreasing.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeDecreasing", "snake_name": "expect_column_values_to_be_decreasing", "short_description": "Expect the column values to be decreasing.", "docstring": "Expect the column values to be decreasing.\n\nBy default, this expectation only works for numeric or datetime data. When 'parse_strings_as_datetimes=True', it can also parse strings to datetimes.\n\nIf 'strictly=True', then this expectation is only satisfied if each consecutive value is strictly decreasing--equal values are treated as failures.\n\nexpect_column_values_to_be_decreasing is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations)\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- strictly (Boolean or None): If True, values must be strictly greater than previous values\n- parse_strings_as_datetimes (boolean or None): If True, all non-null column values to datetimes before making comparisons\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_be_increasing](https://greatexpectations.io/expectations/expect_column_values_to_be_increasing)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.decreasing.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.decreasing.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to be decreasing.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 14.0 }, "expect_column_values_to_be_in_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_in_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeInSet", "snake_name": "expect_column_values_to_be_in_set", "short_description": "Expect each column value to be in a given set.", "docstring": "Expect each column value to be in a given set.\n\n#### For example:\n```\n# my_df.my_col = [1,2,2,3,3,3]\n>>> my_df.expect_column_values_to_be_in_set(\n \"my_col\",\n [2,3]\n )\n{\n \"success\": false\n \"result\": {\n \"unexpected_count\": 1\n \"unexpected_percent\": 16.66666666666666666,\n \"unexpected_percent_nonmissing\": 16.66666666666666666,\n \"partial_unexpected_list\": [\n 1\n ],\n },\n}\n```\n\nexpect_column_values_to_be_in_set is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- value_set (set-like): A set of objects used for comparison.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n- parse_strings_as_datetimes (boolean or None) : If True values provided in value_set will be parsed as datetimes before making comparisons.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_not_be_in_set](https://greatexpectations.io/expectations/expect_column_values_to_not_be_in_set)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.in_set.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.in_set.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 11, "num_failed": 1, "failing_names": [ "all" ] }, { "backend": "mssql", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 15, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 12, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 12 tests for snowflake are passing", "passed": true }, { "message": "All 12 tests for trino are passing", "passed": true }, { "message": "All 12 tests for redshift are passing", "passed": true }, { "message": "All 12 tests for postgresql are passing", "passed": true }, { "message": "All 12 tests for mssql are passing", "passed": true }, { "message": "All 12 tests for spark are passing", "passed": true }, { "message": "All 15 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 12 tests for mysql are passing", "passed": true }, { "message": "Only 11 / 12 tests for bigquery are passing", "passed": false }, { "message": " - Failing: all", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect each column value to be in a given set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 12 tests for snowflake are passing", "passed": true }, { "message": "All 12 tests for trino are passing", "passed": true }, { "message": "All 12 tests for redshift are passing", "passed": true }, { "message": "All 12 tests for postgresql are passing", "passed": true }, { "message": "All 12 tests for mssql are passing", "passed": true }, { "message": "All 12 tests for spark are passing", "passed": true }, { "message": "All 15 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 12 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 131.5 }, "expect_column_values_to_be_in_type_list": { "updated_at": "2023-07-24 12:43:31 -0400 9 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_in_type_list.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeInTypeList", "snake_name": "expect_column_values_to_be_in_type_list", "short_description": "Expect a column to contain values from a specified type list.", "docstring": "Expect a column to contain values from a specified type list.\n\nexpect_column_values_to_be_in_type_list is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations) for typed-column backends, and also for PandasDataset where the column dtype provides an unambiguous constraints (any dtype except 'object').\n\nFor PandasDataset columns with dtype of 'object' expect_column_values_to_be_in_type_list will independently check each row's type.\n\n#### Args:\n- column (str): The column name.\n- type_list (str): A list of strings representing the data type that each column should have as entries. Valid types are defined by the current backend implementation and are dynamically loaded. For example, valid types for PandasDataset include any numpy dtype values (such as 'int64') or native python types (such as 'int'), whereas valid types for a SqlAlchemyDataset include types named by the current driver such as 'INTEGER' in most SQL dialects and 'TEXT' in dialects such as postgresql. Valid types for SparkDFDataset include 'StringType', 'BooleanType' and other pyspark-defined type names.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See also:\n- [expect_column_values_to_be_of_type](https://greatexpectations.io/expectations/expect_column_values_to_be_of_type)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.column_types", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 0, "num_failed": 1, "failing_names": [ "all" ] }, { "backend": "mssql", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 7, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 7 tests for redshift are passing", "passed": true }, { "message": "All 7 tests for postgresql are passing", "passed": true }, { "message": "All 7 tests for mssql are passing", "passed": true }, { "message": "All 7 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 7 tests for sqlite are passing", "passed": true }, { "message": "All 7 tests for mysql are passing", "passed": true }, { "message": "Only 0 / 1 tests for bigquery are passing", "passed": false }, { "message": " - Failing: all", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a column to contain values from a specified type list.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 7 tests for redshift are passing", "passed": true }, { "message": "All 7 tests for postgresql are passing", "passed": true }, { "message": "All 7 tests for mssql are passing", "passed": true }, { "message": "All 7 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 7 tests for sqlite are passing", "passed": true }, { "message": "All 7 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 74.5 }, "expect_column_values_to_be_increasing": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_increasing.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeIncreasing", "snake_name": "expect_column_values_to_be_increasing", "short_description": "Expect the column values to be increasing.", "docstring": "Expect the column values to be increasing.\n\nBy default, this expectation only works for numeric or datetime data. When 'parse_strings_as_datetimes=True', it can also parse strings to datetimes.\n\nIf 'strictly=True', then this expectation is only satisfied if each consecutive value is strictly increasing--equal values are treated as failures.\n\nexpect_column_values_to_be_increasing is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- strictly (Boolean or None): If True, values must be strictly greater than previous values\n- parse_strings_as_datetimes (boolean or None) : If True, all non-null column values to datetimes before making comparisons\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without\n- modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_be_decreasing](https://greatexpectations.io/expectations/expect_column_values_to_be_decreasing)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.increasing.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.increasing.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 7, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 7 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to be increasing.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 7 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 17.0 }, "expect_column_values_to_be_json_parseable": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_json_parseable.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeJsonParseable", "snake_name": "expect_column_values_to_be_json_parseable", "short_description": "Expect the column entries to be data written in JavaScript Object Notation.", "docstring": "Expect the column entries to be data written in JavaScript Object Notation.\n\nexpect_column_values_to_be_json_parseable is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_json_schema](https://greatexpectations.io/expectations/expect_column_values_to_match_json_schema)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.json_parseable.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.json_parseable.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be data written in JavaScript Object Notation.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 14.0 }, "expect_column_values_to_be_null": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_null.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeNull", "snake_name": "expect_column_values_to_be_null", "short_description": "Expect the column values to be null.", "docstring": "Expect the column values to be null.\n\nexpect_column_values_to_be_null is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_not_be_null](https://greatexpectations.io/expectations/expect_column_values_to_not_be_null)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.null.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.null.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 6 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for mssql are passing", "passed": true }, { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 6 tests for sqlite are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to be null.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 6 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for mssql are passing", "passed": true }, { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 6 tests for sqlite are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 73.0 }, "expect_column_values_to_be_of_type": { "updated_at": "2023-06-29 02:36:24 -0700 5 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_of_type.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeOfType", "snake_name": "expect_column_values_to_be_of_type", "short_description": "Expect a column to contain values of a specified data type.", "docstring": "Expect a column to contain values of a specified data type.\n\nexpect_column_values_to_be_of_type is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations) for typed-column backends, and also for PandasDataset where the column dtype and provided type_ are unambiguous constraints (any dtype except 'object' or dtype of 'object' with type_ specified as 'object').\n\nFor PandasDataset columns with dtype of 'object' expect_column_values_to_be_of_type will\nindependently check each row's type.\n\n#### Args:\n- column (str): The column name.\n- type\\_ (str): A string representing the data type that each column should have as entries. Valid types are defined by the current backend implementation and are dynamically loaded. For example, valid types for PandasDataset include any numpy dtype values (such as 'int64') or native python types (such as 'int'), whereas valid types for a SqlAlchemyDataset include types named by the current driver such as 'INTEGER' in most SQL dialects and 'TEXT' in dialects such as postgresql. Valid types for SparkDFDataset include 'StringType', 'BooleanType' and other pyspark-defined type names. Note that the strings representing these types are sometimes case-sensitive. For instance, with a Pandas backend `timestamp` will be unrecognized and\nfail the expectation, while `Timestamp` would pass with valid data.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See also:\n- [expect_column_values_to_be_in_type_list](https://greatexpectations.io/expectations/expect_column_values_to_be_in_type_list)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.column_types", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 1, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 1, "num_failed": 3, "failing_names": [ "basic_sqlalchemy_int_positive_test", "positive_test_sql_varchar", "negative_test_sqlalchemy_floats_are_not_boolean" ] }, { "backend": "redshift", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 1 tests for snowflake are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 2 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 12 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true }, { "message": "Only 1 / 4 tests for trino are passing", "passed": false }, { "message": " - Failing: basic_sqlalchemy_int_positive_test, positive_test_sql_varchar, negative_test_sqlalchemy_floats_are_not_boolean", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect a column to contain values of a specified data type.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 1 tests for snowflake are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 2 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 12 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 51.5 }, "expect_column_values_to_be_unique": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_be_unique.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "EXPERIMENTAL", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToBeUnique", "snake_name": "expect_column_values_to_be_unique", "short_description": "Expect each column value to be unique.", "docstring": "Expect each column value to be unique.\n\nThis expectation detects duplicates. All duplicated values are counted as exceptions.\n\nFor example, [1, 2, 3, 3, 3] will return [3, 3, 3] in result.exceptions_list, with unexpected_percent = 60.0.\n\nexpect_column_values_to_be_unique is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations)\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.unique.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.unique.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 0, "num_failed": 1, "failing_names": [ "all" ] }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": false, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 7 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 11 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true }, { "message": "Only 0 / 1 tests for bigquery are passing", "passed": false }, { "message": " - Failing: all", "passed": false } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect each column value to be unique.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 7 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 11 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 86.5 }, "expect_column_values_to_match_json_schema": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_match_json_schema.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchJsonSchema", "snake_name": "expect_column_values_to_match_json_schema", "short_description": "Expect the column entries to be JSON objects matching a given JSON schema.", "docstring": "Expect the column entries to be JSON objects matching a given JSON schema.\n\nexpect_column_values_to_match_json_schema is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- json_schema (str): The JSON schema (in string form) to match\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_be_json_parseable](https://greatexpectations.io/expectations/expect_column_values_to_be_json_parseable)\n- [The JSON-schema docs](https://json-schema.org)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.match_json_schema.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.match_json_schema.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 3, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for spark are passing", "passed": true }, { "message": "All 3 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be JSON objects matching a given JSON schema.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for spark are passing", "passed": true }, { "message": "All 3 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 10.0 }, "expect_column_values_to_match_like_pattern": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_match_like_pattern.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchLikePattern", "snake_name": "expect_column_values_to_match_like_pattern", "short_description": "Expect the column entries to be strings that match a given like pattern expression.", "docstring": "Expect the column entries to be strings that match a given like pattern expression.\n\nexpect_column_values_to_match_like_pattern is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- like_pattern (str): The like pattern expression the column entries should match.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.match_like_pattern.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.match_like_pattern.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 10, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that match a given like pattern expression.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 88.0 }, "expect_column_values_to_match_like_pattern_list": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_match_like_pattern_list.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchLikePatternList", "snake_name": "expect_column_values_to_match_like_pattern_list", "short_description": "Expect the column entries to be strings that match any of a provided list of like pattern expressions.", "docstring": "Expect the column entries to be strings that match any of a provided list of like pattern expressions.\n\nexpect_column_values_to_match_like_pattern_list is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- like_pattern_list (List[str]): The list of like pattern expressions the column entries should match.\n\n#### Keyword Args:\n- match_on (string): \"any\" or \"all\". Use \"any\" if the value should match at least one like pattern in the list. Use \"all\" if it should match each like pattern in the list.\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.match_like_pattern_list.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.match_like_pattern_list.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for mssql are passing", "passed": true }, { "message": "All 6 tests for sqlite are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that match any of a provided list of like pattern expressions.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for mssql are passing", "passed": true }, { "message": "All 6 tests for sqlite are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 56.0 }, "expect_column_values_to_match_regex": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_match_regex.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchRegex", "snake_name": "expect_column_values_to_match_regex", "short_description": "Expect the column entries to be strings that match a given regular expression.", "docstring": "Expect the column entries to be strings that match a given regular expression.\n\nValid matches can be found anywhere in the string, for example \"[at]+\" will identify the following strings as expected: \"cat\", \"hat\", \"aa\", \"a\", and \"t\", and the following strings as unexpected: \"fish\", \"dog\".\n\nexpect_column_values_to_match_regex is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- regex (str): The regular expression the column entries should match.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.match_regex.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.match_regex.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 13, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 11, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 12 tests for trino are passing", "passed": true }, { "message": "All 12 tests for redshift are passing", "passed": true }, { "message": "All 12 tests for postgresql are passing", "passed": true }, { "message": "All 11 tests for bigquery are passing", "passed": true }, { "message": "All 12 tests for spark are passing", "passed": true }, { "message": "All 13 tests for pandas are passing", "passed": true }, { "message": "All 11 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that match a given regular expression.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 12 tests for trino are passing", "passed": true }, { "message": "All 12 tests for redshift are passing", "passed": true }, { "message": "All 12 tests for postgresql are passing", "passed": true }, { "message": "All 11 tests for bigquery are passing", "passed": true }, { "message": "All 12 tests for spark are passing", "passed": true }, { "message": "All 13 tests for pandas are passing", "passed": true }, { "message": "All 11 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 104.0 }, "expect_column_values_to_match_regex_list": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_match_regex_list.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchRegexList", "snake_name": "expect_column_values_to_match_regex_list", "short_description": "Expect the column entries to be strings that can be matched to either any of or all of a list of regular expressions.", "docstring": "Expect the column entries to be strings that can be matched to either any of or all of a list of regular expressions.\n\nMatches can be anywhere in the string.\n\nexpect_column_values_to_match_regex_list is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- regex_list (list): The list of regular expressions which the column entries should match\n\n#### Keyword Args:\n- match_on (string): \"any\" or \"all\". Use \"any\" if the value should match at least one regular expression in the list. Use \"all\" if it should match each regular expression in the list.\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.match_regex_list.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.match_regex_list.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that can be matched to either any of or all of a list of regular expressions.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 58.0 }, "expect_column_values_to_match_strftime_format": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_match_strftime_format.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToMatchStrftimeFormat", "snake_name": "expect_column_values_to_match_strftime_format", "short_description": "Expect the column entries to be strings representing a date or time with a given format.", "docstring": "Expect the column entries to be strings representing a date or time with a given format.\n\nexpect_column_values_to_match_strftime_format is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- strftime_format (str): A strftime format string to use for matching\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": false, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.match_strftime_format.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.match_strftime_format.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine" ], "backend_test_result_counts": [ { "backend": "spark", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 9, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings representing a date or time with a given format.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true } ] } ] }, "coverage_score": 23.0 }, "expect_column_values_to_not_be_in_set": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_not_be_in_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotBeInSet", "snake_name": "expect_column_values_to_not_be_in_set", "short_description": "Expect column entries to not be in the set.", "docstring": "Expect column entries to not be in the set.\n\n#### For example:\n```\n# my_df.my_col = [1,2,2,3,3,3]\n>>> my_df.expect_column_values_to_not_be_in_set(\n \"my_col\",\n [1,2]\n )\n{\n \"success\": false\n \"result\": {\n \"unexpected_count\": 3\n \"unexpected_percent\": 50.0,\n \"unexpected_percent_nonmissing\": 50.0,\n \"partial_unexpected_list\": [\n 1, 2, 2\n ],\n },\n}\n```\n\nexpect_column_values_to_not_be_in_set is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- value_set (set-like): A set of objects used for comparison.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_be_in_set](https://greatexpectations.io/expectations/expect_column_values_to_be_in_set)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_in_set.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_in_set.unexpected_values", "has_question_renderer": false } ], "errors": [], 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"production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect column entries to not be in the set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 10 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 104.0 }, "expect_column_values_to_not_be_null": { "updated_at": "2023-07-24 12:43:31 -0400 9 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_not_be_null.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotBeNull", "snake_name": "expect_column_values_to_not_be_null", "short_description": "Expect the column values to not be null.", "docstring": "Expect the column values to not be null.\n\nTo be counted as an exception, values must be explicitly null or missing, such as a NULL in PostgreSQL or an\nnp.NaN in pandas. Empty strings don't count as null unless they have been coerced to a null type.\n\nexpect_column_values_to_not_be_null is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_be_null](https://greatexpectations.io/expectations/expect_column_values_to_be_null)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.nonnull.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 7, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 7, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 7 tests for snowflake are passing", "passed": true }, { "message": "All 7 tests for trino are passing", "passed": true }, { "message": "All 7 tests for redshift are passing", "passed": true }, { "message": "All 7 tests for postgresql are passing", "passed": true }, { "message": "All 7 tests for bigquery are passing", "passed": true }, { "message": "All 7 tests for mssql are passing", "passed": true }, { "message": "All 7 tests for spark are passing", "passed": true }, { "message": "All 7 tests for pandas are passing", "passed": true }, { "message": "All 7 tests for sqlite are passing", "passed": true }, { "message": "All 7 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column values to not be null.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 7 tests for snowflake are passing", "passed": true }, { "message": "All 7 tests for trino are passing", "passed": true }, { "message": "All 7 tests for redshift are passing", "passed": true }, { "message": "All 7 tests for postgresql are passing", "passed": true }, { "message": "All 7 tests for bigquery are passing", "passed": true }, { "message": "All 7 tests for mssql are passing", "passed": true }, { "message": "All 7 tests for spark are passing", "passed": true }, { "message": "All 7 tests for pandas are passing", "passed": true }, { "message": "All 7 tests for sqlite are passing", "passed": true }, { "message": "All 7 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 83.0 }, "expect_column_values_to_not_match_like_pattern": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_not_match_like_pattern.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotMatchLikePattern", "snake_name": "expect_column_values_to_not_match_like_pattern", "short_description": "Expect the column entries to be strings that do NOT match a given like pattern expression.", "docstring": "Expect the column entries to be strings that do NOT match a given like pattern expression.\n\nexpect_column_values_to_not_match_like_pattern is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- like_pattern (str): The like pattern expression the column entries should NOT match.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_match_like_pattern.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_match_like_pattern.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that do NOT match a given like pattern expression.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 73.0 }, "expect_column_values_to_not_match_like_pattern_list": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_not_match_like_pattern_list.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotMatchLikePatternList", "snake_name": "expect_column_values_to_not_match_like_pattern_list", "short_description": "Expect the column entries to be strings that do NOT match any of a provided list of like pattern expressions.", "docstring": "Expect the column entries to be strings that do NOT match any of a provided list of like pattern expressions.\n\nexpect_column_values_to_not_match_like_pattern_list is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- like_pattern_list (List[str]): The list of like pattern expressions the column entries should NOT match.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n " }, "execution_engines": { "PandasExecutionEngine": false, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": false }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_match_like_pattern_list.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_match_like_pattern_list.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that do NOT match any of a provided list of like pattern expressions.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for mssql are passing", "passed": true }, { "message": "All 4 tests for sqlite are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 41.0 }, "expect_column_values_to_not_match_regex": { "updated_at": "2023-07-24 12:43:31 -0400 9 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_not_match_regex.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotMatchRegex", "snake_name": "expect_column_values_to_not_match_regex", "short_description": "Expect the column entries to be strings that do NOT match a given regular expression.", "docstring": "Expect the column entries to be strings that do NOT match a given regular expression.\n\nThe regex must not match any portion of the provided string. For example, \"[at]+\" would identify the following strings as expected: \"fish\", \"dog\", and the following as unexpected: \"cat\", \"hat\".\n\nexpect_column_values_to_not_match_regex is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- regex (str): The regular expression the column entries should NOT match.\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex_list)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_match_regex.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_match_regex.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that do NOT match a given regular expression.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 4 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 77.0 }, "expect_column_values_to_not_match_regex_list": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_column_values_to_not_match_regex_list.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core ColumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "column map expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectColumnValuesToNotMatchRegexList", "snake_name": "expect_column_values_to_not_match_regex_list", "short_description": "Expect the column entries to be strings that do not match any of a list of regular expressions. Matches can be anywhere in the string.", "docstring": "Expect the column entries to be strings that do not match any of a list of regular expressions. Matches can be anywhere in the string.\n\nexpect_column_values_to_not_match_regex_list is a [Column Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_map_expectations).\n\n#### Args:\n- column (str): The column name.\n- regex_list (list): The list of regular expressions which the column entries should not match\n\n#### Keyword Args:\n- mostly (None or a float between 0 and 1): Successful if at least mostly fraction of values match the expectation. For more detail, see [mostly](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#mostly).\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_column_values_to_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_match_regex)\n- [expect_column_values_to_match_regex_list](https://greatexpectations.io/expectations/expect_column_values_to_match_regex_list)\n- [expect_column_values_to_not_match_regex](https://greatexpectations.io/expectations/expect_column_values_to_not_match_regex)\n- [expect_column_values_to_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern)\n- [expect_column_values_to_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_match_like_pattern_list)\n- [expect_column_values_to_not_match_like_pattern](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern)\n- [expect_column_values_to_not_match_like_pattern_list](https://greatexpectations.io/expectations/expect_column_values_to_not_match_like_pattern_list)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "column_values.nonnull.unexpected_count", "has_question_renderer": false }, { "name": "column_values.not_match_regex_list.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "column_values.not_match_regex_list.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 3, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 4, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 4, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the column entries to be strings that do not match any of a list of regular expressions. Matches can be anywhere in the string.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 3 tests for snowflake are passing", "passed": true }, { "message": "All 4 tests for trino are passing", "passed": true }, { "message": "All 4 tests for redshift are passing", "passed": true }, { "message": "All 4 tests for postgresql are passing", "passed": true }, { "message": "All 4 tests for bigquery are passing", "passed": true }, { "message": "All 4 tests for spark are passing", "passed": true }, { "message": "All 4 tests for pandas are passing", "passed": true }, { "message": "All 4 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 42.0 }, "expect_compound_columns_to_be_unique": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_compound_columns_to_be_unique.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "multi-column expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectCompoundColumnsToBeUnique", "snake_name": "expect_compound_columns_to_be_unique", "short_description": "Expect the compound columns to be unique.", "docstring": "Expect the compound columns to be unique.\n\nexpect_compound_columns_to_be_unique is a [Multicolumn Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_multicolumn_map_expectations).\n\n#### Args:\n- column_list (tuple or list): Set of columns to be checked\n\n#### Keyword Args:\n- ignore_row_if (str): \"all_values_are_missing\", \"any_value_is_missing\", \"never\"\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "compound_columns.unique.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "compound_columns.unique.filtered_row_count", "has_question_renderer": false }, { "name": "compound_columns.unique.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 9, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 11 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the compound columns to be unique.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 9 tests for snowflake are passing", "passed": true }, { "message": "All 9 tests for trino are passing", "passed": true }, { "message": "All 9 tests for redshift are passing", "passed": true }, { "message": "All 9 tests for postgresql are passing", "passed": true }, { "message": "All 9 tests for bigquery are passing", "passed": true }, { "message": "All 9 tests for mssql are passing", "passed": true }, { "message": "All 9 tests for spark are passing", "passed": true }, { "message": "All 11 tests for pandas are passing", "passed": true }, { "message": "All 9 tests for sqlite are passing", "passed": true }, { "message": "All 9 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 105.0 }, "expect_multicolumn_sum_to_equal": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2021-02-05 15:51:54 -0500 2 years, 6 months ago", "path": "great_expectations/expectations/core/expect_multicolumn_sum_to_equal.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "multi-column expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectMulticolumnSumToEqual", "snake_name": "expect_multicolumn_sum_to_equal", "short_description": "Expect that the sum of row values in a specified column list is the same for each row, and equal to a specified sum total.", "docstring": "Expect that the sum of row values in a specified column list is the same for each row, and equal to a specified sum total.\n\nexpect_multicolumn_sum_to_equal is a [Multicolumn Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_multicolumn_map_expectations).\n\n#### Args:\n- column_list (tuple or list): Set of columns to be checked\n- sum_total (int): expected sum of columns\n\n#### Keyword Args:\n- ignore_row_if (str): \"all_values_are_missing\", \"any_value_is_missing\", \"never\"\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "multicolumn_sum.equal.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "multicolumn_sum.equal.filtered_row_count", "has_question_renderer": false }, { "name": "multicolumn_sum.equal.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 11, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 10, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 11 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect that the sum of row values in a specified column list is the same for each row, and equal to a specified sum total.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 11 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 114.0 }, "expect_select_column_values_to_be_unique_within_record": { "updated_at": "2023-06-13 12:48:19 -0400 7 weeks ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_select_column_values_to_be_unique_within_record.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core MulticolumnMapExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "multi-column expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectSelectColumnValuesToBeUniqueWithinRecord", "snake_name": "expect_select_column_values_to_be_unique_within_record", "short_description": "Expect the values for each record to be unique across the columns listed.", "docstring": "Expect the values for each record to be unique across the columns listed.\n\nNote that records can be duplicated.\n\nexpect_select_column_values_to_be_unique_within_record is a [Multicolumn Map Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_multicolumn_map_expectations).\n\n#### For example:\n```\nA B C\n1 1 2 Fail\n1 2 3 Pass\n8 2 7 Pass\n1 2 3 Pass\n4 4 4 Fail\n```\n\n#### Args:\n- column_list (tuple or list): The column names to evaluate\n\n#### Keyword Args:\n- ignore_row_if (str): \"all_values_are_missing\", \"any_value_is_missing\", \"never\"\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "select_column_values.unique.within_record.unexpected_count", "has_question_renderer": false }, { "name": "table.row_count", "has_question_renderer": false }, { "name": "select_column_values.unique.within_record.filtered_row_count", "has_question_renderer": false }, { "name": "select_column_values.unique.within_record.unexpected_values", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 9, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 10, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 10, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the values for each record to be unique across the columns listed.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 10 tests for snowflake are passing", "passed": true }, { "message": "All 10 tests for trino are passing", "passed": true }, { "message": "All 10 tests for redshift are passing", "passed": true }, { "message": "All 10 tests for postgresql are passing", "passed": true }, { "message": "All 10 tests for bigquery are passing", "passed": true }, { "message": "All 10 tests for mssql are passing", "passed": true }, { "message": "All 10 tests for spark are passing", "passed": true }, { "message": "All 9 tests for pandas are passing", "passed": true }, { "message": "All 10 tests for sqlite are passing", "passed": true }, { "message": "All 10 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 112.0 }, "expect_table_column_count_to_be_between": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_column_count_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableColumnCountToBeBetween", "snake_name": "expect_table_column_count_to_be_between", "short_description": "Expect the number of columns to be between two values.", "docstring": "Expect the number of columns to be between two values.\n\nexpect_table_column_count_to_be_between is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Keyword Args:\n- min_value (int or None): The minimum number of columns, inclusive.\n- max_value (int or None): The maximum number of columns, inclusive.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive.\n* If min_value is None, then max_value is treated as an upper bound, and the number of acceptable columns has no minimum.\n* If max_value is None, then min_value is treated as a lower bound, and the number of acceptable columns has no maximum.\n\n#### See Also:\n- [expect_table_column_count_to_equal](https://greatexpectations.io/expectations/expect_table_column_count_to_equal)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.column_count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 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example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 93.0 }, "expect_table_column_count_to_equal": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_column_count_to_equal.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableColumnCountToEqual", "snake_name": "expect_table_column_count_to_equal", "short_description": "Expect the number of columns to equal a value.", "docstring": "Expect the number of columns to equal a value.\n\nexpect_table_column_count_to_equal is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- value (int): The expected number of columns.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_table_column_count_to_be_between](https://greatexpectations.io/expectations/expect_table_column_count_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.column_count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "trino", 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"doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of columns to equal a value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 63.0 }, "expect_table_columns_to_match_ordered_list": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_columns_to_match_ordered_list.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableColumnsToMatchOrderedList", "snake_name": "expect_table_columns_to_match_ordered_list", "short_description": "Expect the columns to exactly match a specified list.", "docstring": "Expect the columns to exactly match a specified list.\n\nexpect_table_columns_to_match_ordered_list is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- column_list (list of str): The column names, in the correct order.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.columns", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 6, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 6, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 6 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for mssql are passing", "passed": true }, { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 6 tests for sqlite are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the columns to exactly match a specified list.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 6 tests for snowflake are passing", "passed": true }, { "message": "All 6 tests for trino are passing", "passed": true }, { "message": "All 6 tests for redshift are passing", "passed": true }, { "message": "All 6 tests for postgresql are passing", "passed": true }, { "message": "All 6 tests for bigquery are passing", "passed": true }, { "message": "All 6 tests for mssql are passing", "passed": true }, { "message": "All 6 tests for spark are passing", "passed": true }, { "message": "All 6 tests for pandas are passing", "passed": true }, { "message": "All 6 tests for sqlite are passing", "passed": true }, { "message": "All 6 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 73.0 }, "expect_table_columns_to_match_set": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_columns_to_match_set.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableColumnsToMatchSet", "snake_name": "expect_table_columns_to_match_set", "short_description": "Expect the columns to match an unordered set.", "docstring": "Expect the columns to match an unordered set.\n\nexpect_table_columns_to_match_set is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- column_set (list of str): The column names, in any order.\n- exact_match (boolean): Whether the list of columns must exactly match the observed columns.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.columns", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 12, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 12, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 12 tests for snowflake are passing", "passed": true }, { "message": "All 12 tests for trino are passing", "passed": true }, { "message": "All 12 tests for redshift are passing", "passed": true }, { "message": "All 12 tests for postgresql are passing", "passed": true }, { "message": "All 12 tests for bigquery are passing", "passed": true }, { "message": "All 12 tests for mssql are passing", "passed": true }, { "message": "All 12 tests for spark are passing", "passed": true }, { "message": "All 12 tests for pandas are passing", "passed": true }, { "message": "All 12 tests for sqlite are passing", "passed": true }, { "message": "All 12 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the columns to match an unordered set.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 12 tests for snowflake are passing", "passed": true }, { "message": "All 12 tests for trino are passing", "passed": true }, { "message": "All 12 tests for redshift are passing", "passed": true }, { "message": "All 12 tests for postgresql are passing", "passed": true }, { "message": "All 12 tests for bigquery are passing", "passed": true }, { "message": "All 12 tests for mssql are passing", "passed": true }, { "message": "All 12 tests for spark are passing", "passed": true }, { "message": "All 12 tests for pandas are passing", "passed": true }, { "message": "All 12 tests for sqlite are passing", "passed": true }, { "message": "All 12 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 133.0 }, "expect_table_row_count_to_be_between": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_row_count_to_be_between.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableRowCountToBeBetween", "snake_name": "expect_table_row_count_to_be_between", "short_description": "Expect the number of rows to be between two values.", "docstring": "Expect the number of rows to be between two values.\n\nexpect_table_row_count_to_be_between is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Keyword Args:\n- min_value (int or None): The minimum number of rows, inclusive.\n- max_value (int or None): The maximum number of rows, inclusive.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### Notes:\n* min_value and max_value are both inclusive.\n* If min_value is None, then max_value is treated as an upper bound, and the number of acceptable rows has no minimum.\n* If max_value is None, then min_value is treated as a lower bound, and the number of acceptable rows has no maximum.\n\n#### See Also:\n- [expect_table_row_count_to_equal](https://greatexpectations.io/expectations/expect_table_row_count_to_equal)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.row_count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 8, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 8, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of rows to be between two values.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 8 tests for snowflake are passing", "passed": true }, { "message": "All 8 tests for trino are passing", "passed": true }, { "message": "All 8 tests for redshift are passing", "passed": true }, { "message": "All 8 tests for postgresql are passing", "passed": true }, { "message": "All 8 tests for bigquery are passing", "passed": true }, { "message": "All 8 tests for mssql are passing", "passed": true }, { "message": "All 8 tests for spark are passing", "passed": true }, { "message": "All 8 tests for pandas are passing", "passed": true }, { "message": "All 8 tests for sqlite are passing", "passed": true }, { "message": "All 8 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 93.0 }, "expect_table_row_count_to_equal": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_row_count_to_equal.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableRowCountToEqual", "snake_name": "expect_table_row_count_to_equal", "short_description": "Expect the number of rows to equal a value.", "docstring": "Expect the number of rows to equal a value.\n\nexpect_table_row_count_to_equal is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- value (int): The expected number of rows.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_table_row_count_to_be_between](https://greatexpectations.io/expectations/expect_table_row_count_to_be_between)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.row_count", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "spark", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "pandas", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 5, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 5, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Custom 'assert' statements in validate_configuration", "passed": true } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of rows to equal a value.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 5 tests for snowflake are passing", "passed": true }, { "message": "All 5 tests for trino are passing", "passed": true }, { "message": "All 5 tests for redshift are passing", "passed": true }, { "message": "All 5 tests for postgresql are passing", "passed": true }, { "message": "All 5 tests for bigquery are passing", "passed": true }, { "message": "All 5 tests for mssql are passing", "passed": true }, { "message": "All 5 tests for spark are passing", "passed": true }, { "message": "All 5 tests for pandas are passing", "passed": true }, { "message": "All 5 tests for sqlite are passing", "passed": true }, { "message": "All 5 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 63.0 }, "expect_table_row_count_to_equal_other_table": { "updated_at": "2023-07-25 15:50:41 -0500 7 days ago", "created_at": "2020-12-01 10:37:14 -0700 2 years, 8 months ago", "path": "great_expectations/expectations/core/expect_table_row_count_to_equal_other_table.py", "package": "core", "requirements": [], "import_module_args": [], "sys_path": "", "exp_type": "Core BatchExpectation", "gallery_examples": [], "library_metadata": { "maturity": "PRODUCTION", "tags": [ "core expectation", "table expectation", "multi-table expectation" ], "contributors": [ "@great_expectations" ], "requirements": [], "library_metadata_passed_checks": true, "has_full_test_suite": true, "manually_reviewed_code": true, "problems": [] }, "description": { "camel_name": "ExpectTableRowCountToEqualOtherTable", "snake_name": "expect_table_row_count_to_equal_other_table", "short_description": "Expect the number of rows to equal the number in another table.", "docstring": "Expect the number of rows to equal the number in another table.\n\nexpect_table_row_count_to_equal_other_table is a [Batch Expectation](https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_batch_expectations).\n\n#### Args:\n- other_table_name (str): The name of the other table.\n\n#### Other Parameters:\n- result_format (str or None): Which output mode to use: BOOLEAN_ONLY, BASIC, COMPLETE, or SUMMARY. For more detail, see [result_format](https://docs.greatexpectations.io/docs/reference/expectations/result_format).\n- include_config (boolean): If True, then include the expectation config as part of the result object.\n- catch_exceptions (boolean or None): If True, then catch exceptions and include them as part of the result object. For more detail, see [catch_exceptions](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#catch_exceptions).\n- meta (dict or None): A JSON-serializable dictionary (nesting allowed) that will be included in the output without modification. For more detail, see [meta](https://docs.greatexpectations.io/docs/reference/expectations/standard_arguments/#meta).\n\n#### Returns:\n- An [ExpectationSuiteValidationResult](https://docs.greatexpectations.io/docs/terms/validation_result)\n\nExact fields vary depending on the values passed to result_format, include_config, catch_exceptions, and meta.\n\n#### See Also:\n- [expect_table_row_count_to_be_between](https://greatexpectations.io/expectations/expect_table_row_count_to_be_between)\n- [expect_table_row_count_to_equal](https://greatexpectations.io/expectations/expect_table_row_count_to_equal)\n " }, "execution_engines": { "PandasExecutionEngine": true, "SqlAlchemyExecutionEngine": true, "SparkDFExecutionEngine": true }, "renderers": [], "metrics": [ { "name": "table.row_count.self", "has_question_renderer": false }, { "name": "table.row_count.other", "has_question_renderer": false } ], "errors": [], "execution_engines_list": [ "PandasExecutionEngine", "SparkDFExecutionEngine", "SqlAlchemyExecutionEngine" ], "backend_test_result_counts": [ { "backend": "snowflake", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "trino", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "redshift", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "postgresql", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "bigquery", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "mssql", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "sqlite", "num_passed": 2, "num_failed": 0, "failing_names": null }, { "backend": "mysql", "num_passed": 2, "num_failed": 0, "failing_names": null } ], "maturity_checklist": { "production": [ { "message": "Has a full suite of tests, as determined by a code owner", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has passed a manual review by a code owner for code standards and style guides", "passed": true, "doc_url": null, "sub_messages": [] } ], "beta": [ { "message": "Has basic input validation and type checking", "passed": true, "doc_url": null, "sub_messages": [ { "message": "Using default validate_configuration from template", "passed": false } ] }, { "message": "Has both statement Renderers: prescriptive and diagnostic", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic that passes tests for all applicable Execution Engines and SQL dialects", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for snowflake are passing", "passed": true }, { "message": "All 2 tests for trino are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 2 tests for postgresql are passing", "passed": true }, { "message": "All 2 tests for bigquery are passing", "passed": true }, { "message": "All 2 tests for mssql are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true }, { "message": "All 2 tests for mysql are passing", "passed": true } ] } ], "experimental": [ { "message": "Has a valid library_metadata object", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has a docstring, including a one-line short description that begins with \"Expect\" and ends with a period", "passed": true, "doc_url": null, "sub_messages": [ { "message": "\"Expect the number of rows to equal the number in another table.\"", "passed": true } ] }, { "message": "Has at least one positive and negative example case, and all test cases pass", "passed": true, "doc_url": null, "sub_messages": [] }, { "message": "Has core logic and passes tests on at least one Execution Engine", "passed": true, "doc_url": null, "sub_messages": [ { "message": "All 2 tests for snowflake are passing", "passed": true }, { "message": "All 2 tests for trino are passing", "passed": true }, { "message": "All 2 tests for redshift are passing", "passed": true }, { "message": "All 2 tests for postgresql are passing", "passed": true }, { "message": "All 2 tests for bigquery are passing", "passed": true }, { "message": "All 2 tests for mssql are passing", "passed": true }, { "message": "All 2 tests for sqlite are passing", "passed": true }, { "message": "All 2 tests for mysql are passing", "passed": true } ] } ] }, "coverage_score": 27.0 } }