{"id":25158338,"url":"https://github.com/m-kovalsky/fabric_cat_tools","last_synced_at":"2025-04-30T10:43:12.874Z","repository":{"id":228961975,"uuid":"775061931","full_name":"m-kovalsky/fabric_cat_tools","owner":"m-kovalsky","description":"Supercharge your Microsoft Fabric development with the fabric_cat_tools library","archived":true,"fork":false,"pushed_at":"2024-07-23T06:47:21.000Z","size":2099,"stargazers_count":117,"open_issues_count":1,"forks_count":16,"subscribers_count":10,"default_branch":"main","last_synced_at":"2025-04-30T10:43:02.391Z","etag":null,"topics":["analysis-services","analysis-services-tabular","fabric","microsoft","notebook","powerbi","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/m-kovalsky.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-03-20T17:31:40.000Z","updated_at":"2025-03-20T18:07:47.000Z","dependencies_parsed_at":"2024-04-15T14:53:51.302Z","dependency_job_id":null,"html_url":"https://github.com/m-kovalsky/fabric_cat_tools","commit_stats":null,"previous_names":["m-kovalsky/fabric_cat_tools"],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-kovalsky%2Ffabric_cat_tools","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-kovalsky%2Ffabric_cat_tools/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-kovalsky%2Ffabric_cat_tools/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/m-kovalsky%2Ffabric_cat_tools/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/m-kovalsky","download_url":"https://codeload.github.com/m-kovalsky/fabric_cat_tools/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251684867,"owners_count":21627205,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analysis-services","analysis-services-tabular","fabric","microsoft","notebook","powerbi","python"],"created_at":"2025-02-09T01:49:53.603Z","updated_at":"2025-04-30T10:43:12.852Z","avatar_url":"https://github.com/m-kovalsky.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003e [!IMPORTANT]\n\u003e This library has been archived and replaced by [Semantic Link Labs](https://github.com/microsoft/semantic-link-labs/). Everything in the fabric_cat_tools library has been moved to the Semantic Link Labs library and will be maintained there going forward. Semantic Link Labs is fully open-sourced and is part of Microsoft's official GitHub repositories.\n\n\n\n___\n___\n___\n___\n___\n# Function Categories\n\n### Semantic Model\n* [clear_cache](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#clear_cache)\n* [create_semantic_model_from_bim](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#create_semantic_model_from_bim)\n* [get_semantic_model_bim](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_semantic_model_bim)\n* [get_measure_dependencies](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_measure_dependencies)\n* [get_model_calc_dependencies](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_model_calc_dependencies)\n* [measure_dependency_tree](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#measure_dependency_tree)\n* [refresh_semantic_model](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#refresh_semantic_model)\n* [cancel_dataset_refresh](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#cancel_dataset_refresh)\n* [run_dax](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#run_dax)\n* [get_object_level_security](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_object_level_security)\n* [translate_semantic_model](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#translate_semantic_model)\n* [list_semantic_model_objects](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_semantic_model_objects)\n\n### Report\n* [report_rebind](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#report_rebind)\n* [report_rebind_all](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#report_rebind_all)\n* [create_report_from_reportjson](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#create_report_from_reportjson)\n* [get_report_json](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_report_json)\n* [export_report](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#export_report)\n* [clone_report](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#clone_report)\n* [list_dashboards](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_dashboards)\n* [launch_report](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#launch_report)\n* [generate_embedded_filter](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#generate_embedded_filter)\n\n### Model Optimization\n* [vertipaq_analyzer](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#vertipaq_analyzer)\n* [import_vertipaq_analyzer](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#import_vertipaq_analyzer)\n* [run_model_bpa](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#run_model_bpa)\n* [model_bpa_rules](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#model_bpa_rules)\n\n### Direct Lake Migration\n* [create_pqt_file](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#create_pqt_file)\n* [create_blank_semantic_model](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#create_blank_semantic_model)\n* [migrate_field_parameters](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#migrate_field_parameters)\n* [migrate_tables_columns_to_semantic_model](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#migrate_tables_columns_to_semantic_model)\n* [migrate_calc_tables_to_semantic_model](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#migrate_calc_tables_to_semantic_model)\n* [migrate_model_objects_to_semantic_model](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#migrate_model_objects_to_semantic_model)\n* [migrate_calc_tables_to_lakehouse](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#migrate_calc_tables_to_lakehouse)\n* [refresh_calc_tables](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#refresh_calc_tables)\n* [show_unsupported_direct_lake_objects](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#show_unsupported_direct_lake_objects)\n* [update_direct_lake_partition_entity](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#update_direct_lake_partition_entity)\n* [update_direct_lake_model_lakehouse_connection](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#update_direct_lake_model_lakehouse_connection)\n* [migration_validation](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#migration_validation)\n\n### Direct Lake\n* [check_fallback_reason](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#check_fallback_reason)\n* [control_fallback](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#control_fallback)\n* [direct_lake_schema_compare](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#direct_lake_schema_compare)\n* [direct_lake_schema_sync](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#direct_lake_schema_sync)\n* [get_direct_lake_lakehouse](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_direct_lake_lakehouse)\n* [get_directlake_guardrails_for_sku](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_directlake_guardrails_for_sku)\n* [get_direct_lake_guardrails](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_direct_lake_guardrails)\n* [get_shared_expression](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_shared_expression)\n* [get_direct_lake_sql_endpoint](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_direct_lake_sql_endpoint)\n* [get_sku_size](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_sku_size)\n* [list_direct_lake_model_calc_tables](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_direct_lake_model_calc_tables)\n* [warm_direct_lake_cache_perspective](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#warm_direct_lake_cache_perspective)\n* [warm_direct_lake_cache_isresident](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#warm_direct_lake_cache_isresident)\n\n### Lakehouse\n* [get_lakehouse_tables](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_lakehouse_tables)\n* [get_lakehouse_columns](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_lakehouse_columns)\n* [list_lakehouses](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_lakehouses)\n* [export_model_to_onelake](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#export_model_to_onelake)\n* [create_shortcut_onelake](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#create_shortcut_onelake)\n* [delete_shortcut](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#delete_shortcut)\n* [list_shortcuts](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_shortcuts)\n* [optimize_lakehouse_tables](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#optimize_lakehouse_tables)\n* [create_warehouse](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#create_warehouse)\n* [update_item](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#update_item)\n* [list_dataflow_storage_accounts](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_dataflow_storage_accounts)\n* [list_warehouses](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#list_warehouses)\n* [save_as_delta_table](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#save_as_delta_table)\n\n### Helper Functions\n* [resolve_dataset_id](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#resolve_dataset_id)\n* [resolve_dataset_name](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#resolve_dataset_name)\n* [resolve_lakehouse_id](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#resolve_lakehouse_id)\n* [resolve_lakehouse_name](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#resolve_lakehouse_name)\n* [resolve_report_id](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#resolve_report_id)\n* [resolve_report_name](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-files#resolve_report_name)\n\n### [Tabular Object Model](https://learn.microsoft.com/analysis-services/tom/introduction-to-the-tabular-object-model-tom-in-analysis-services-amo?view=asallproducts-allversions) ([TOM](https://learn.microsoft.com/dotnet/api/microsoft.analysisservices.tabular.model?view=analysisservices-dotnet))\n#### 'All' functions for non-parent objects within TOM\n* [all_columns](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_columns)\n* [all_measures](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_measures)\n* [all_partitions](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_partitions)\n* [all_hierarchies](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_hierarchies)\n* [all_levels](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_levels)\n* [all_calculation_items](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_calculation_items)\n* [all_rls](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#all_rls)\n\n#### 'Add' functions\n* [add_calculated_column](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_calculated_column)\n* [add_calculated_table](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_calculated_table)\n* [add_calculated_table_column](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_calculated_table_column)\n* [add_calculation_group](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_calculation_group)\n* [add_calculation_item](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_calculation_item)\n* [add_data_column](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_data_column)\n* [add_entity_partition](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_entity_partition)\n* [add_expression](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_expression)\n* [add_field_parameter](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_field_parameter)\n* [add_hierarchy](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_hierarchy)\n* [add_m_partition](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_m_partition)\n* [add_measure](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_measure)\n* [add_perspective](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_perspective)\n* [add_relationship](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_relationship)\n* [add_role](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_role)\n* [add_table](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_table)\n* [add_translation](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_translation)\n\n#### 'Set' functions\n* [set_direct_lake_behavior](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_direct_lake_behavior)\n* [set_is_available_in_mdx](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_is_available_in_mdx)\n* [set_ols](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_ols)\n* [set_rls](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_rls)\n* [set_summarize_by](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_summarize_by)\n* [set_translation](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_translation)\n\n#### 'Remove' functions\n* [remove_object](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#remove_object)\n* [remove_translation](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#remove_translation)\n\n#### 'Used-in' and dependency functions\n* [used_in_relationships](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_in_relationships)\n* [used_in_hierarchies](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_in_hierarchies)\n* [used_in_levels](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_in_levels)\n* [used_in_sort_by](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_in_sort_by)\n* [used_in_rls](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_in_rls)\n* [used_in_calc_item](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_in_calc_item)\n* [depends_on](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#depends_on)\n* [referenced_by](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#referenced_by)\n* [fully_qualified_measures](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#fully_qualified_measures)\n* [unqualified_columns](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#unqualified_columns)\n\n#### Vertipaq Analyzer data functions\n* [remove_vertipaq_annotations](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#remove_vertipaq_annotations)\n* [set_vertipaq_annotations](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_vertipaq_annotations)\n* [row_count](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#row_count)\n* [used_size](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#used_size)\n* [data_size](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#data_size)\n* [dictionary_size](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#dictionary_size)\n* [total_size](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#total_size)\n* [cardinality](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#cardinality)\n\n#### Perspectives\n* [in_perspective](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#in_perspective)\n* [add_to_perspective](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#add_to_perspective)\n* [remove_from_perspective](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#remove_from_perspective)\n  \n#### Annotations\n* [get_annotations](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_annotations)\n* [set_annotation](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_annotation)\n* [get_annotation_value](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_annotation_value)\n* [remove_annotation](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#remove_annotation)\n* [clear_annotations](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#clear_annotations)\n\n#### Extended Properties\n* [get_extended_properties](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_extended_properties)\n* [set_extended_property](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#set_extended_property)\n* [get_extended_property_value](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#get_extended_property_value)\n* [remove_extended_property](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#remove_extended_property)\n* [clear_extended_properties](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#clear_extended_properties)\n\n#### Misc\n* [is_direct_lake](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#is_direct_lake)\n* [is_field_parameter](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#is_field_parameter)\n\n\n# Functions\n## cancel_dataset_refresh\n#### Cancels the refresh of a semantic model which was executed via the [Enhanced Refresh API](https://learn.microsoft.com/power-bi/connect-data/asynchronous-refresh).\n```python\nimport fabric_cat_tools as fct\nfct.cancel_dataset_refresh(\n            dataset = 'MyReport',\n            #request_id = None,\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **request_id** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The [request id](https://learn.microsoft.com/power-bi/connect-data/asynchronous-refresh#response-properties) of a semantic model refresh. Defaults to finding the latest active refresh of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## check_fallback_reason\n#### Shows the reason a table in a Direct Lake semantic model would fallback to Direct Query.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.check_fallback_reason(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e Pandas dataframe showing the tables in the semantic model and their fallback reason.\n\n---\n## clear_cache\n#### Clears the cache of a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.clear_cache(\n            dataset = 'AdventureWorks',\n            #workspace = '' \n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## clone_report\n#### Makes a clone of a Power BI report\n```python\nimport fabric_cat_tools as fct\nfct.clone_report(\n            report = 'MyReport',\n            cloned_report = 'MyNewReport',\n            #workspace = None,\n            #target_workspace = None,\n            #target_dataset = None\n            )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the report to be cloned.\n\u003e\n\u003e **cloned_report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the new report.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the original report resides.\n\u003e\n\u003e **target_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the new report will reside. Defaults to using the workspace in which the original report resides.\n\u003e\n\u003e **target_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The semantic model from which the new report will be connected. Defaults to using the semantic model used by the original report.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## control_fallback\n#### Set the DirectLakeBehavior for a semantic model.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.control_fallback(\n            dataset = 'AdventureWorks',\n            direct_lake_behavior = 'DirectLakeOnly',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **direct_lake_behavior** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Setting for Direct Lake Behavior. Options: ('Automatic', 'DirectLakeOnly', 'DirectQueryOnly').\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## create_blank_semantic_model\n#### Creates a new blank semantic model (no tables/columns etc.).\n```python\nimport fabric_cat_tools as fct\nfct.create_blank_semantic_model(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **compatibility_level** [int](https://docs.python.org/3/library/functions.html#int)\n\u003e \n\u003e\u003e Optional; Setting for the compatibility level of the semantic model. Default value: 1605.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## create_pqt_file\n#### Dynamically generates a [Power Query Template](https://learn.microsoft.com/power-query/power-query-template) file based on the semantic model. The .pqt file is saved within the Files section of your lakehouse.\n```python\nimport fabric_cat_tools as fct\nfct.create_pqt_file(\n            dataset = 'AdventureWorks',\n            #file_name = 'PowerQueryTemplate',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **file_name** [str](https://docs.python.org/3/library/functions.html#str)\n\u003e \n\u003e\u003e Optional; TName of the Power Query Template (.pqt) file to be created.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## create_report_from_reportjson\n#### Creates a report based on a report.json file (and an optional themes.json file).\n```python\nimport fabric_cat_tools as fct\nfct.create_report_from_reportjson(\n            report = 'MyReport',\n            dataset = 'AdventureWorks',\n            report_json = '',\n            #theme_json = '',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the report.\n\u003e\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model to connect to the report.\n\u003e\n\u003e **report_json** [Dict](https://docs.python.org/3/library/typing.html#typing.Dict) or [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The report.json file to be used to create the report.\n\u003e \n\u003e **theme_json** [Dict](https://docs.python.org/3/library/typing.html#typing.Dict) or [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The theme.json file to be used for the theme of the report.\n\u003e \n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## create_semantic_model_from_bim\n#### Creates a new semantic model based on a Model.bim file.\n```python\nimport fabric_cat_tools as fct\nfct.create_semantic_model_from_bim(\n            dataset = 'AdventureWorks',\n            bim_file = '',\n            #workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **bim_file** [Dict](https://docs.python.org/3/library/typing.html#typing.Dict) or [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The model.bim file to be used to create the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## create_shortcut_onelake\n#### Creates a [shortcut](https://learn.microsoft.com/fabric/onelake/onelake-shortcuts) to a delta table in OneLake.\n```python\nimport fabric_cat_tools as fct\nfct.create_shortcut_onelake(\n            table_name = 'DimCalendar',\n            source_lakehouse = 'Lakehouse1',\n            source_workspace = 'Workspace1',\n            destination_lakehouse = 'Lakehouse2',\n            #destination_workspace = '',\n            shortcut_name = 'Calendar'\n            )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The table name for which a shortcut will be created.\n\u003e\n\u003e **source_lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The lakehouse in which the table resides.\n\u003e\n\u003e **sourceWorkspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The workspace where the source lakehouse resides.\n\u003e\n\u003e **destination_lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The lakehouse where the shortcut will be created.\n\u003e\n\u003e **destination_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace in which the shortcut will be created. Defaults to the 'sourceWorkspaceName' parameter value.\n\u003e\n\u003e **shortcut_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The name of the shortcut 'table' to be created. This defaults to the 'tableName' parameter value.\n\u003e\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## create_warehouse\n#### Creates a warehouse in Fabric.\n```python\nimport fabric_cat_tools as fct\nfct.create_warehouse(\n            warehouse = 'MyWarehouse',\n            workspace = None\n            )\n```\n### Parameters\n\u003e **warehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the warehouse.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/functions.html#str)\n\u003e \n\u003e\u003e Optional; Description of the warehouse.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the warehouse will reside.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## delete_shortcut\n#### Deletes a [OneLake shortcut](https://learn.microsoft.com/fabric/onelake/onelake-shortcuts).\n```python\nimport fabric_cat_tools as fct\nfct.delete_shortcut(\n            shortcut_name = 'DimCalendar',\n            lakehouse = 'Lakehouse1',\n            workspace = 'Workspace1'\n            )\n```\n### Parameters\n\u003e **shortcut_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The name of the OneLake shortcut to delete.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse in which the shortcut resides.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n\u003e\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## direct_lake_schema_compare\n#### Checks that all the tables in a Direct Lake semantic model map to tables in their corresponding lakehouse and that the columns in each table exist.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.direct_lake_schema_compare(\n            dataset = 'AdventureWorks',\n            workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace in which the lakehouse resides.\n\u003e\n### Returns\n\u003e Shows tables/columns which exist in the semantic model but do not exist in the corresponding lakehouse.\n\n---\n## direct_lake_schema_sync\n#### Shows/adds columns which exist in the lakehouse but do not exist in the semantic model (only for tables in the semantic model).\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.direct_lake_schema_sync(\n     dataset = 'AdvWorks',\n     add_to_model = True,\n    #workspace = '',\n    #lakehouse = '',\n    #lakehouse_workspace = ''\n    )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **add_to_model** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Adds columns which exist in the lakehouse but do not exist in the semantic model. No new tables are added. Default value: False.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace in which the lakehouse resides.\n\u003e\n### Returns\n\u003e A list of columns which exist in the lakehouse but not in the Direct Lake semantic model. If 'add_to_model' is set to True, a printout stating the success/failure of the operation is returned.\n\n---\n## export_model_to_onelake\n#### Exports a semantic model's tables to delta tables in the lakehouse. Creates shortcuts to the tables if a lakehouse is specified.\n\u003e [!IMPORTANT]\n\u003e This function requires:\n\u003e \n\u003e [XMLA read/write](https://learn.microsoft.com/power-bi/enterprise/service-premium-connect-tools#enable-xmla-read-write) to be enabled on the Fabric capacity.\n\u003e \n\u003e [OneLake Integration](https://learn.microsoft.com/power-bi/enterprise/onelake-integration-overview#enable-onelake-integration) feature to be enabled within the semantic model settings.\n```python\nimport fabric_cat_tools as fct\nfct.export_model_to_onelake(\n            dataset = 'AdventureWorks',\n            workspace = None,\n            destination_lakehouse = 'Lakehouse2',\n            destination_workspace = 'Workspace2'\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **destination_lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse where shortcuts will be created to access the delta tables created by the export. If the lakehouse specified does not exist, one will be created with that name. If no lakehouse is specified, shortcuts will not be created.\n\u003e\n\u003e **destination_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace in which the lakehouse resides.\n\u003e\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## export_report\n#### Exports a Power BI report to a file in your lakehouse.\n```python\nimport fabric_cat_tools as fct\nfct.export_report(\n            report = 'AdventureWorks',\n            export_format = 'PDF',\n            #file_name = None,\n            #bookmark_name = None,\n            #page_name = None,\n            #visual_name = None,\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.export_report(\n            report = 'AdventureWorks',\n            export_format = 'PDF',\n            #file_name = 'Exports\\MyReport',\n            #bookmark_name = None,\n            #page_name = 'ReportSection293847182375',\n            #visual_name = None,\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.export_report(\n            report = 'AdventureWorks',\n            export_format = 'PDF',\n            #page_name = 'ReportSection293847182375',\n            #report_filter = \"'Product Category'[Color] in ('Blue', 'Orange') and 'Calendar'[CalendarYear] \u003c= 2020\",\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.export_report(\n            report = 'AdventureWorks',\n            export_format = 'PDF',\n            #page_name = ['ReportSection293847182375', 'ReportSection4818372483347'],\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.export_report(\n            report = 'AdventureWorks',\n            export_format = 'PDF',\n            #page_name = ['ReportSection293847182375', 'ReportSection4818372483347'],\n            #visual_name = ['d84793724739', 'v834729234723847'],\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **export_format** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The format in which to export the report. See this link for valid formats: https://learn.microsoft.com/rest/api/power-bi/reports/export-to-file-in-group#fileformat. For image formats, enter the file extension in this parameter, not 'IMAGE'.\n\u003e\n\u003e **file_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The name of the file to be saved within the lakehouse. Do **not** include the file extension. Defaults ot the reportName parameter value.\n\u003e\n\u003e **bookmark_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The name (GUID) of a bookmark within the report.\n\u003e\n\u003e **page_name** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The name (GUID) of the report page.\n\u003e\n\u003e **visual_name** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The name (GUID) of a visual. If you specify this parameter you must also specify the page_name parameter.\n\u003e\n\u003e **report_filter** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; A report filter to be applied when exporting the report. Syntax is user-friendly. See above for examples.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the report resides.\n\u003e\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## generate_embedded_filter\n#### Runs a DAX query against a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.generate_embedded_filter(\n            filter = \"'Product'[Product Category] = 'Bikes' and 'Geography'[Country Code] in (3, 6, 10)\"       \n            )\n```\n### Parameters\n\u003e **filter** [str](https://docs.python.org/3/library/stdtypes.html#str)\n### Returns\n\u003e A string converting the filter into an [embedded filter](https://learn.microsoft.com/power-bi/collaborate-share/service-url-filters)\n\n---\n## get_direct_lake_guardrails\n#### Shows the guardrails for when Direct Lake semantic models will fallback to Direct Query based on Microsoft's online documentation.\n```python\nimport fabric_cat_tools as fct\nfct.get_direct_lake_guardrails()\n```\n### Parameters\nNone\n### Returns\n\u003e A table showing the Direct Lake guardrails by SKU.\n\n---\n## get_directlake_guardrails_for_sku\n#### Shows the guardrails for Direct Lake based on the SKU used by your workspace's capacity.\n*Use the result of the 'get_sku_size' function as an input for this function's skuSize parameter.*\n```python\nimport fabric_cat_tools as fct\nfct.get_directlake_guardrails_for_sku(\n            sku_size = ''\n            )\n```\n### Parameters\n\u003e **sku_size** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Sku size of a workspace/capacity\n### Returns\n\u003e A table showing the Direct Lake guardrails for the given SKU.\n\n---\n## get_direct_lake_lakehouse\n#### Identifies the lakehouse used by a Direct Lake semantic model.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.get_direct_lake_lakehouse(\n            dataset = 'AdventureWorks',\n            #workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''            \n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Name of the lakehouse used by the semantic model.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n\n---\n## get_direct_lake_sql_endpoint\n#### Identifies the lakehouse used by a Direct Lake semantic model.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.get_direct_lake_sql_endpoint(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A string containing the SQL Endpoint ID for a Direct Lake semantic model.\n\n---\n## get_lakehouse_columns\n#### Shows the tables and columns of a lakehouse and their respective properties.\n```python\nimport fabric_cat_tools as fct\nfct.get_lakehouse_columns(\n            lakehouse = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse name.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A pandas dataframe showing the tables/columns within a lakehouse and their properties.\n\n---\n## get_lakehouse_tables\n#### Shows the tables of a lakehouse and their respective properties. Option to include additional properties relevant to Direct Lake guardrails.\n```python\nimport fabric_cat_tools as fct\nfct.get_lakehouse_tables(\n        lakehouse = 'MyLakehouse',\n        workspace = 'NewWorkspace',\n        extended = True,\n        count_rows = True)\n```\n### Parameters\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse name.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n\u003e\n\u003e **extended** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Adds the following additional table properties \\['Files', 'Row Groups', 'Table Size', 'Parquet File Guardrail', 'Row Group Guardrail', 'Row Count Guardrail'\\]. Also indicates the SKU for the workspace and whether guardrails are hit. Default value: False.\n\u003e\n\u003e **count_rows** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Adds an additional column showing the row count of each table. Default value: False.\n\u003e\n\u003e **export** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; If specified as True, the resulting dataframe will be exported to a delta table in your lakehouse.\n### Returns\n\u003e A pandas dataframe showing the delta tables within a lakehouse and their properties.\n\n---\n## get_measure_dependencies\n#### Shows all dependencies for all measures in a semantic model\n```python\nimport fabric_cat_tools as fct\nfct.get_measure_dependencies(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A pandas dataframe showing all dependencies for all measures in the semantic model.\n\n---\n## get_model_calc_dependencies\n#### Shows all dependencies for all objects in a semantic model\n```python\nimport fabric_cat_tools as fct\nfct.get_model_calc_dependencies(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A pandas dataframe showing all dependencies for all objects in the semantic model.\n\n---\n## get_object_level_security\n#### Shows a list of columns used in object level security.\n```python\nimport fabric_cat_tools as fct\nfct.get_object_level_security(\n        dataset = 'AdventureWorks',\n        workspace = '')\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The semantic model name.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A pandas dataframe showing the columns used in object level security within a semantic model.\n\n---\n## get_report_json\n#### Gets the report.json file content of a Power BI report.\n```python\nimport fabric_cat_tools as fct\nfct.get_report_json(\n            report = 'MyReport',\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.get_report_json(\n            report = 'MyReport',\n            #workspace = None,\n            save_to_file_name = 'MyFileName'\n            )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the report.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the report resides.\n\u003e\n\u003e **save_to_file_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Specifying this parameter will save the report.json file to your lakehouse with the file name of this parameter.\n### Returns\n\u003e The report.json file for a given Power BI report.\n\n---\n## get_semantic_model_bim\n#### Extracts the Model.bim file for a given semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.get_semantic_model_bim(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.get_semantic_model_bim(\n            dataset = 'AdventureWorks',\n            #workspace = None,\n            save_to_file_name = 'MyFileName'\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **save_to_file_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Specifying this parameter will save the model.bim file to your lakehouse with the file name of this parameter.\n### Returns\n\u003e The model.bim file for a given semantic model.\n\n---\n## get_shared_expression\n#### Dynamically generates the M expression used by a Direct Lake model for a given lakehouse.\n```python\nimport fabric_cat_tools as fct\nfct.get_shared_expression(\n            lakehouse = '',\n            #workspace = '' \n            )\n```\n### Parameters\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse name.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A string showing the expression which can be used to connect a Direct Lake semantic model to its SQL Endpoint.\n\n---\n## get_sku_size\n#### Shows the SKU size for a workspace.\n```python\nimport fabric_cat_tools as fct\nfct.get_sku_size(\n            workspace = '' \n            )\n```\n### Parameters\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A string containing the SKU size for a workspace.\n\n---\n## import_vertipaq_analyzer\n#### Imports and visualizes the vertipaq analyzer info from a saved .zip file in your lakehouse.\n```python\nimport fabric_cat_tools as fct\nfct.import_vertipaq_analyzer(\n          folder_path = '/lakehouse/default/Files/VertipaqAnalyzer',\n          file_name = 'Workspace Name-DatasetName.zip'\n          )\n```\n### Parameters\n\u003e **folder_path** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; Folder within your lakehouse in which the .zip file containing the vertipaq analyzer info has been saved.\n\u003e\n\u003e **file_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; File name of the file which contains the vertipaq analyzer info.\n\n---\n## launch_report\n#### Shows a Power BI report within a Fabric notebook.\n```python\nimport fabric_cat_tools as fct\nfct.launch_report(\n          report = 'MyReport',\n          #workspace = None\n          )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the report.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The name of the workspace in which the report resides.\n\n---\n## list_dashboards\n#### Shows the dashboards within the workspace.\n```python\nimport fabric_cat_tools as fct\nfct.list_dashboards(\n            #workspace = '' \n            )\n```\n### Parameters\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace name.\n### Returns\n\u003e A pandas dataframe showing the dashboards which exist in the workspace.\n\n---\n## list_dataflow_storage_accounts\n#### Shows the dataflow storage accounts.\n```python\nimport fabric_cat_tools as fct\nfct.list_dataflow_storage_accounts()\n```\n### Parameters\nNone\n### Returns\n\u003e A pandas dataframe showing the accessible dataflow storage accounts.\n\u003e \n---\n## list_direct_lake_model_calc_tables\n#### Shows the calculated tables and their respective DAX expression for a Direct Lake model (which has been migrated from import/DirectQuery.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.list_direct_lake_model_calc_tables(\n            dataset = 'AdventureWorks',\n            #workspace = '' \n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A pandas dataframe showing the calculated tables which were migrated to Direct Lake and whose DAX expressions are stored as model annotations.\n\n---\n## list_lakehouses\n#### Shows the properties associated with lakehouses in a workspace.\n```python\nimport fabric_cat_tools as fct\nfct.list_lakehouses(\n            workspace = None\n            )\n```\n### Parameters\n\u003e **workspaceName** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A pandas dataframe showing the properties of a all lakehouses in a workspace.\n\n---\n## list_semantic_model_objects\n#### Shows a list of semantic model objects.\n```python\nimport fabric_cat_tools as fct\nfct.list_semantic_model_objects(\n            dataset = 'AdvWorks',\n            workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A dataframe showing a list of objects in the semantic model\n\n---\n## list_shortcuts\n#### Shows the shortcuts within a lakehouse (*note: the API behind this function is not yet available. The function will work as expected once the API is officially released*)\n```python\nimport fabric_cat_tools as fct\nfct.list_shortcuts(\n            lakehouse = 'MyLakehouse',\n            #workspace = '' \n            )\n```\n### Parameters\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Name of the lakehouse.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A pandas dataframe showing the shortcuts which exist in a given lakehouse and their properties.\n\n---\n## list_warehouses\n#### Shows the warehouss within a workspace.\n```python\nimport fabric_cat_tools as fct\nfct.list_warehouses(\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace name.\n### Returns\n\u003e A pandas dataframe showing the warehouses which exist in a given workspace and their properties.\n\n---\n## measure_dependency_tree\n#### Shows a measure dependency tree of all dependent objects for a measure in a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.measure_dependency_tree(\n            dataset = 'AdventureWorks',\n            measure_name = 'Sales Amount',\n            #workspace = '' \n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **measure_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the measure to use for building a dependency tree.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A tree view showing the dependencies for a given measure within the semantic model.\n\n---\n## migrate_calc_tables_to_lakehouse\n#### Creates delta tables in your lakehouse based on the DAX expression of a calculated table in an import/DirectQuery semantic model. The DAX expression encapsulating the calculated table logic is stored in the new Direct Lake semantic model as model annotations.\n\u003e [!NOTE]\n\u003e This function is specifically relevant for import/DirectQuery migration to Direct Lake\n```python\nimport fabric_cat_tools as fct\nfct.migrate_calc_tables_to_lakehouse(\n            dataset = 'AdventureWorks',\n            new_dataset = 'AdventureWorksDL',\n            #workspace = '',\n            #new_dataset_workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the Direct Lake semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace to be used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse to be used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## migrate_calc_tables_to_semantic_model\n#### Creates new tables in the Direct Lake semantic model based on the lakehouse tables created using the 'migrate_calc_tables_to_lakehouse' function.\n\u003e [!NOTE]\n\u003e This function is specifically relevant for import/DirectQuery migration to Direct Lake\n```python\nimport fabric_cat_tools as fct\nfct.migrate_calc_tables_to_semantic_model(\n            dataset = 'AdventureWorks',\n            new_dataset = 'AdventureWorksDL',\n            #workspace = '',\n            #new_dataset_workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the Direct Lake semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace to be used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse to be used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## migrate_field_parameters\n#### Migrates field parameters from one semantic model to another.\n\u003e [!NOTE]\n\u003e This function is specifically relevant for import/DirectQuery migration to Direct Lake\n```python\nimport fabric_cat_tools as fct\nfct.migrate_field_parameters(\n            dataset = 'AdventureWorks',\n            new_dataset = '',\n            #workspace = '',\n            #new_dataset_workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the Direct Lake semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace to be used by the Direct Lake semantic model.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## migrate_model_objects_to_semantic_model\n#### Adds the rest of the model objects (besides tables/columns) and their properties to a Direct Lake semantic model based on an import/DirectQuery semantic model.\n\u003e [!NOTE]\n\u003e This function is specifically relevant for import/DirectQuery migration to Direct Lake\n```python\nimport fabric_cat_tools as fct\nfct.migrate_model_objects_to_semantic_model(\n            dataset = 'AdventureWorks',\n            new_dataset = '',\n            #workspace = '',\n            #new_dataset_workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the Direct Lake semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace to be used by the Direct Lake semantic model.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## migrate_tables_columns_to_semantic_model\n#### Adds tables/columns to the new Direct Lake semantic model based on an import/DirectQuery semantic model.\n\u003e [!NOTE]\n\u003e This function is specifically relevant for import/DirectQuery migration to Direct Lake\n```python\nimport fabric_cat_tools as fct\nfct.migrate_tables_columns_to_semantic_model(\n            dataset = 'AdventureWorks',\n            new_dataset = 'AdventureWorksDL',\n            #workspace = '',\n            #new_dataset_workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the Direct Lake semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace to be used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The lakehouse to be used by the Direct Lake semantic model.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## migration_validation\n#### Shows the objects in the original semantic model and whether then were migrated successfully or not.\n```python\nimport fabric_cat_tools as fct\nfct.migration_validation(\n            dataset = 'AdvWorks',\n            new_dataset = 'AdvWorksDL',\n            workspace = None,\n            new_dataset_workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the import/DirectQuery semantic model.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the Direct Lake semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace to be used by the Direct Lake semantic model.\n### Returns\n\u003e A dataframe showing a list of objects and whether they were successfully migrated. Also shows the % of objects which were migrated successfully.\n\n---\n## model_bpa_rules\n#### Shows the default Best Practice Rules for the semantic model used by the [run_model_bpa](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#run_model_bpa) function\n```python\nimport fabric_cat_tools as fct\nfct.model_bpa_rules()\n```\n### Returns\n\u003e A pandas dataframe showing the default semantic model best practice rules.\n\n---\n## optimize_lakehouse_tables\n#### Runs the [OPTIMIZE](https://docs.delta.io/latest/optimizations-oss.html) function over the specified lakehouse tables.\n```python\nimport fabric_cat_tools as fct\nfct.optimize_lakehouse_tables(\n            tables = ['Sales', 'Calendar'],\n            #lakehouse = None,\n            #workspace = None\n        )\n```\n```python\nimport fabric_cat_tools as fct\nfct.optimize_lakehouse_tables(\n            tables = None,\n            #lakehouse = 'MyLakehouse',\n            #workspace = None\n        )\n```\n### Parameters\n\u003e **tables** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name(s) of the lakehouse delta table(s) to optimize. If 'None' is entered, all of the delta tables in the lakehouse will be queued to be optimized.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Name of the lakehouse.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## refresh_calc_tables\n#### Recreates the delta tables in the lakehouse based on the DAX expressions stored as model annotations in the Direct Lake semantic model.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.refresh_calc_tables(\n            dataset = 'AdventureWorks',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## refresh_semantic_model\n#### Performs a refresh on a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.refresh_semantic_model(\n    dataset = 'AdventureWorks',\n    refresh_type = 'full',\n    workspace = None\n)\n```\n```python\nimport fabric_cat_tools as fct\nfct.refresh_semantic_model(\n    dataset = 'AdventureWorks',\n    tables = ['Sales', 'Geography'],\n    workspace = None\n)\n```\n```python\nimport fabric_cat_tools as fct\nfct.refresh_semantic_model(\n    dataset = 'AdventureWorks',\n    partitions = [\"'Sales'[Sales - 2024]\", \"'Sales'[Sales - 2023]\"],\n    workspace = None\n)\n```\n```python\nimport fabric_cat_tools as fct\nfct.refresh_semantic_model(\n    dataset = 'AdventureWorks',\n    tables = ['Geography'],\n    partitions = [\"'Sales'[Sales - 2024]\", \"'Sales'[Sales - 2023]\"],\n    workspace = None\n)\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model. If no tables/partitions are specified, the entire semantic model is refreshed.\n\u003e\n\u003e **tables** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Tables to refresh.\n\u003e\n\u003e **partitions** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Partitions to refresh. Must be in \"'Table'[Partition]\" format.\n\u003e\n\u003e **refresh_type** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Type of processing to perform. Options: ('full', 'automatic', 'dataOnly', 'calculate', 'clearValues', 'defragment'). Default value: 'full'.\n\u003e\n\u003e **retry_count** [int](https://docs.python.org/3/library/stdtypes.html#int)\n\u003e \n\u003e\u003e Optional; Number of retry attempts. Default is 0.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## report_rebind\n#### Rebinds a report to a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.report_rebind(\n            report = '',\n            dataset = '',\n            #report_workspace = '',\n            #dataset_workspace = ''\n            )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the report.\n\u003e\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model to rebind to the report.\n\u003e\n\u003e **report_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the report resides.\n\u003e\n\u003e **dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## report_rebind_all\n#### Rebinds all reports in a workspace which are bound to a specific semantic model to a new semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.report_rebind_all(\n            dataset = '',\n            new_dataset = '',\n            #dataset_workspace = '' ,\n            #new_dataset_workspace = '' ,\n            #report_workspace = '' \n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model currently binded to the reports.\n\u003e\n\u003e **new_dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model to rebind to the reports.\n\u003e\n\u003e **dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the original semantic model resides.\n\u003e\n\u003e **new_dataset_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the new semantic model resides.\n\u003e\n\u003e **report_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the reports reside.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## resolve_lakehouse_name\n#### Returns the name of the lakehouse for a given lakehouse Id.\n```python\nimport fabric_cat_tools as fct\nfct.resolve_lakehouse_name(\n        lakehouse_id = '',\n        #workspace = '' \n        )\n```\n### Parameters\n\u003e **lakehouse_id** [UUID](https://docs.python.org/3/library/uuid.html#uuid.UUID)\n\u003e \n\u003e\u003e Required; UUID object representing a lakehouse.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A string containing the lakehouse name.\n\n---\n## resolve_lakehouse_id\n#### Returns the ID of a given lakehouse.\n```python\nimport fabric_cat_tools as fct\nfct.resolve_lakehouse_id(\n        lakehouse = 'MyLakehouse',\n        #workspace = '' \n        )\n```\n### Parameters\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the lakehouse.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A string conaining the lakehouse ID.\n\n---\n## resolve_dataset_id\n#### Returns the ID of a given semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.resolve_dataset_id(\n        dataset = 'MyReport',\n        #workspace = '' \n        )\n```\n### Parameters\n\u003e **datasetName** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspaceName** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A string containing the semantic model ID.\n\n---\n## resolve_dataset_name\n#### Returns the name of a given semantic model ID.\n```python\nimport fabric_cat_tools as fct\nfct.resolve_dataset_name(\n        dataset_id = '',\n        #workspace = '' \n        )\n```\n### Parameters\n\u003e **dataset_id** [UUID](https://docs.python.org/3/library/uuid.html#uuid.UUID)\n\u003e \n\u003e\u003e Required; UUID object representing a semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A string containing the semantic model name.\n\n---\n## resolve_report_id\n#### Returns the ID of a given report.\n```python\nimport fabric_cat_tools as fct\nfct.resolve_report_id(\n        report = 'MyReport',\n        #workspace = '' \n        )\n```\n### Parameters\n\u003e **report** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the report.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the report resides.\n### Returns\n\u003e A string containing the report ID.\n\n---\n## resolve_report_name\n#### Returns the name of a given report ID.\n```python\nimport fabric_cat_tools as fct\nfct.resolve_report_name(\n        report_id = '',\n        #workspace = '' \n        )\n```\n### Parameters\n\u003e **report_id** [UUID](https://docs.python.org/3/library/uuid.html#uuid.UUID)\n\u003e \n\u003e\u003e Required; UUID object representing a report.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the report resides.\n### Returns\n\u003e A string containing the report name.\n\n---\n## run_dax\n#### Runs a DAX query against a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.run_dax(\n            dataset = 'AdventureWorks',\n            dax_query = 'Internet Sales',\n            user_name = 'hello@goodbye.com',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **dax_query** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; The DAX query to be executed.\n\u003e\n\u003e **user_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A pandas dataframe with the results of the DAX query.\n\n---\n## run_model_bpa\n#### Runs the Best Practice Rules against a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.run_model_bpa(\n        dataset = 'AdventureWorks',\n        #workspace = None\n        )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **rules_dataframe**\n\u003e \n\u003e\u003e Optional; A pandas dataframe including rules to be analyzed.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **return_dataframe** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Returns a pandas dataframe instead of the visualization.\n\u003e\n\u003e **export** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Exports the results to a delta table in the lakehouse.\n### Returns\n\u003e A visualization showing objects which violate each [Best Practice Rule](https://github.com/microsoft/Analysis-Services/tree/master/BestPracticeRules) by rule category.\n\nSeverity: Info: ℹ️ Warning: ⚠️ Error: ❌\n\n\n---\n## save_as_delta_table\n#### Saves a dataframe as a delta table in the lakehouse\n```python\nimport fabric_cat_tools as fct\nfct.save_as_delta_table(\n            dataframe = df,\n            delta_table_name = 'MyNewTable',\n            write_mode = 'overwrite',\n            lakehouse = None,\n            workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.save_as_delta_table(\n            dataframe = df,\n            delta_table_name = 'MyNewTable',\n            write_mode = 'append',\n            lakehouse = None,\n            workspace = None\n            )\n```\n### Parameters\n\u003e **dataframe** [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)\n\u003e \n\u003e\u003e Required; The dataframe to save as a delta table.\n\u003e\n\u003e **delta_table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the delta table to save the dataframe.\n\u003e\n\u003e **write_mode** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; Options: 'append' or 'overwrite'.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional: The name of the lakehouse in which the delta table will be saved. Defaults to the default lakehouse attached to the notebook.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The workspace where the lakehouse resides. Defaults to the workspace in which the notebook resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## show_unsupported_direct_lake_objects\n#### Returns a list of a semantic model's objects which are not supported by Direct Lake based on [official documentation](https://learn.microsoft.com/power-bi/enterprise/directlake-overview#known-issues-and-limitations).\n```python\nimport fabric_cat_tools as fct\nfct.show_unsupported_direct_lake_objects(\n        dataset = 'AdventureWorks',\n        #workspace = None\n        )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e 3 [pandas dataframes](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) showing objects (tables/columns/relationships) within the semantic model which are currently not supported by Direct Lake mode.\n\n---\n## translate_semantic_model\n#### Translates names, descriptions, display folders for all objects in a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.translate_semantic_model(\n            dataset = 'AdventureWorks',\n            languages = ['it-IT', 'fr-FR'],\n            #workspace = None\n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.translate_semantic_model(\n            dataset = 'AdventureWorks',\n            languages = ['it_IT', 'fr-FR'],\n            exclude_characters = '_-',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **languages** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; [Language code(s)](https://learn.microsoft.com/azure/ai-services/translator/language-support) to translate.\n\u003e\n\u003e **exclude_characters** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; Any character in this string will be replaced by a space when given to the AI translator.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## update_direct_lake_model_lakehouse_connection\n#### Remaps a Direct Lake semantic model's SQL Endpoint connection to a new lakehouse.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.update_direct_lake_model_lakehouse_connection(\n            dataset = '',\n            #lakehouse = '',\n            #workspace = ''\n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Name of the lakehouse.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## update_direct_lake_partition_entity\n#### Remaps a table (or tables) in a Direct Lake semantic model to a table in a lakehouse.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.update_direct_lake_partition_entity(\n            dataset = 'AdventureWorks',\n            table_name = 'Internet Sales',\n            entity_name = 'FACT_InternetSales',\n            #workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''            \n            )\n```\n```python\nimport fabric_cat_tools as fct\nfct.update_direct_lake_partition_entity(\n            dataset = 'AdventureWorks',\n            table_name = ['Internet Sales', 'Geography'],\n            entity_name = ['FACT_InternetSales', 'DimGeography'],\n            #workspace = '',\n            #lakehouse = '',\n            #lakehouse_workspace = ''            \n            )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the table in the semantic model.\n\u003e\n\u003e **entity_name** [str](https://docs.python.org/3/library/stdtypes.html#str) or [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the lakehouse table to be mapped to the semantic model table.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **lakehouse** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Name of the lakehouse.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the lakehouse resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## update_item\n#### Creates a warehouse in Fabric.\n```python\nimport fabric_cat_tools as fct\nfct.update_item(\n            item_type = 'Lakehouse',\n            current_name = 'MyLakehouse',\n            new_name = 'MyNewLakehouse',\n            #description = 'This is my new lakehouse',\n            #workspace = None\n            )\n```\n### Parameters\n\u003e **item_type** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Type of item to update. Valid options: 'DataPipeline', 'Eventstream', 'KQLDatabase', 'KQLQueryset', 'Lakehouse', 'MLExperiment', 'MLModel', 'Notebook', 'Warehouse'.\n\u003e\n\u003e **current_name** [str](https://docs.python.org/3/library/functions.html#str)\n\u003e \n\u003e\u003e Required; Current name of the item.\n\u003e\n\u003e **new_name** [str](https://docs.python.org/3/library/functions.html#str)\n\u003e \n\u003e\u003e Required; New name of the item.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/functions.html#str)\n\u003e \n\u003e\u003e Optional; New description of the item.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the item resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## vertipaq_analyzer\n#### Extracts the vertipaq analyzer statistics from a semantic model.\n```python\nimport fabric_cat_tools as fct\nfct.vertipaq_analyzer(\n        dataset = 'AdventureWorks',\n        #workspace = '',\n        export = None\n        )\n```\n\n```python\nimport fabric_cat_tools as fct\nfct.vertipaq_analyzer(\n        dataset = 'AdventureWorks',\n        #workspace = '',\n        export = 'zip'\n        )\n```\n\n```python\nimport fabric_cat_tools as fct\nfct.vertipaq_analyzer(\n        dataset = 'AdventureWorks',\n        #workspace = '',\n        export = 'table'\n        )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n\u003e\n\u003e **export** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; Specifying 'zip' will export the results to a zip file in your lakehouse (which can be imported using the [import_vertipaq_analyzer](https://github.com/m-kovalsky/fabric_cat_tools?tab=readme-ov-file#import_vertipaq_analyzer) function. Specifying 'table' will export the results to delta tables (appended) in your lakehouse. Default value: None.\n\u003e\n\u003e **lakehouse_workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace in which the lakehouse used by a Direct Lake semantic model resides.\n\u003e\n\u003e **read_stats_from_data** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Setting this parameter to true has the function get Column Cardinality and Missing Rows using DAX (Direct Lake semantic models achieve this using a Spark query to the lakehouse).\n### Returns\n\u003e A visualization of the Vertipaq Analyzer statistics.\n\n---\n## warm_direct_lake_cache_perspective\n#### Warms the cache of a Direct Lake semantic model by running a simple DAX query against the columns in a perspective\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.warm_direct_lake_cache_perspective(\n        dataset = 'AdventureWorks',\n        perspective = 'WarmCache',\n        add_dependencies = True,\n        #workspace = None\n        )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **perspective** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the perspective which contains objects to be used for warming the cache.\n\u003e\n\u003e **add_dependencies** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e \n\u003e\u003e Optional; Includes object dependencies in the cache warming process.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n\n---\n## warm_direct_lake_cache_isresident\n#### Performs a refresh on the semantic model and puts the columns which were in memory prior to the refresh back into memory.\n\u003e [!NOTE]\n\u003e This function is only relevant to semantic models in Direct Lake mode.\n```python\nimport fabric_cat_tools as fct\nfct.warm_direct_lake_cache_isresident(\n        dataset = 'AdventureWorks',\n        #workspace = None\n        )\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Required; Name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e \n\u003e\u003e Optional; The workspace where the semantic model resides.\n### Returns\n\u003e A printout stating the success/failure of the operation.\n---\n\n# fabric_cat_tools.TOM Functions\n\n## connect_semantic_model\n#### Forms the connection to the Tabular Object Model (TOM) for a semantic model\n```python\nwith connect_semantic_model(dataset ='AdventureWorks', workspace = None, readonly = True) as tom:\n```\n```python\nwith connect_semantic_model(dataset ='AdventureWorks', workspace = None, readonly = False) as tom:\n```\n### Parameters\n\u003e **dataset** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the semantic model.\n\u003e\n\u003e **workspace** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The name of the workspace in which the semantic model resides. Defaults to the workspace in which the notebook resides.\n\u003e\n\u003e **readonly** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Setting this to true uses a read only mode of TOM. Setting this to false enables read/write and saves any changes made to the semantic model. Default value: True.\n\n## add_calculated_column\n#### Adds a calculated column to a table within a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_calculated_column(\n        table_name = 'Segment',\n        column_name = 'Business Segment',\n        expression = '',\n        data_type = 'String'\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table where the column will be added.\n\u003e\n\u003e **column_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the calculated column.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The DAX expression for the calculated column.\n\u003e\n\u003e **data_type** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The data type of the calculated column.\n\u003e\n\u003e **format_string** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The formats strinf for the column.\n\u003e\n\u003e **hidden** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Sets the column to be hidden if True. Default value: False.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the column.\n\u003e\n\u003e **display_folder** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The display folder for the column.\n\u003e\n\u003e **data_category** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The data category of the column.\n\u003e\n\u003e **key** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Marks the column as the primary key of the table. Default value: False.\n\u003e\n\u003e **summarize_by** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; Sets the value for the Summarize By property of the column.\n\u003e\n### Returns\n\u003e \n\n---\n## add_calculated_table\n#### Adds a calculated table to a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_calculated_table(\n        name = 'Segment',\n        expression = ''\n    )\n```\n### Parameters\n\u003e **name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The DAX expression for the table.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the table.\n\u003e\n\u003e **data_category** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The data category of the table.\n\u003e\n\u003e **hidden** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Sets the table to be hidden if True. Default value: False.\n\u003e\n### Returns\n\u003e \n\n---\n## add_calculated_table_column\n#### Adds a calculated table column to a calculated table within a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_calculated_table_column(\n        table_name = 'Segment',\n        column_name = 'Business Segment',\n        source_column = '',\n        data_type = 'String'\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table in which the column will reside.\n\u003e\n\u003e **column_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the column.\n\u003e\n\u003e **source_column** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The source column for the column.\n\u003e\n\u003e **data_type** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The data type of the column.\n\u003e\n\u003e **format_string** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The format string of the column.\n\u003e\n\u003e **hidden** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Sets the column to be hidden if True. Default value: False.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the column.\n\u003e\n\u003e **display_folder** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The display folder for the column.\n\u003e\n\u003e **data_category** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The data category of the column.\n\u003e\n\u003e **key** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Marks the column as the primary key of the table. Default value: False.\n\u003e\n\u003e **summarize_by** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; Sets the value for the Summarize By property of the column.\n\u003e\n### Returns\n\u003e \n\n---\n## add_calculation_group\n#### Adds a calculation group to a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_calculation_group(\n        name = 'Segment',\n        precedence = 1\n    )\n```\n### Parameters\n\u003e **name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the calculation group.\n\u003e\n\u003e **precedence** [int](https://docs.python.org/3/library/stdtypes.html#int)\n\u003e\n\u003e\u003e Optional;\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the calculation group.\n\u003e\n\u003e **hidden** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Sets the calculation group to be hidden if True. Default value: False.\n\u003e\n### Returns\n\u003e \n\n---\n## add_calculation_item\n#### Adds a calculation item to a calculation group within a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_calculation_item(\n        table_name = 'Segment',\n        calculation_item_name = 'YTD'\n        expression = \"CALCULATE(SELECTEDMEASURE(), DATESYTD('Date'[Date]))\"\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table.\n\u003e\n\u003e **calculation_item_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the calculation item.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The DAX expression encapsulating the logic of the calculation item.\n\u003e\n\u003e **ordinal** [int](https://docs.python.org/3/library/stdtypes.html#int)\n\u003e\n\u003e\u003e Optional;\n\u003e\n\u003e **format_string_expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional;\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the calculation item.\n\u003e\n### Returns\n\u003e \n\n---\n## add_data_column\n#### Adds a data column to a table within a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_data_column(\n        table_name = 'Segment',\n        column_name = 'Business Segment',\n        source_column = '',\n        data_type = 'String'\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table in which the column will exist.\n\u003e\n\u003e **column_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the column.\n\u003e\n\u003e **source_column** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the column in the source.\n\u003e\n\u003e **data_type** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The data type of the column.\n\u003e\n\u003e **format_string** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The format string of the column.\n\u003e\n\u003e **hidden** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Sets the column to be hidden if True. Default value: False.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the column.\n\u003e\n\u003e **display_folder** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The display folder for the column.\n\u003e\n\u003e **data_category** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The data category of the column.\n\u003e\n\u003e **key** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Marks the column as the primary key of the table. Default value: False.\n\u003e\n\u003e **summarize_by** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; Sets the value for the Summarize By property of the column.\n\u003e\n### Returns\n\u003e \n\n---\n## add_entity_partition\n#### Adds an entity partition to a table in a semantic model. Entity partitions are used for tables within Direct Lake semantic models.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_entity_partition(\n        table_name = 'Sales',\n        entity_name = 'Fact_Sales'\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table in which to place the entity partition.\n\u003e\n\u003e **entity_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the lakehouse table.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The expression to use for the partition. This defaults to using the existing 'DatabaseQuery' expression within the Direct Lake semantic model.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the partition.\n\u003e\n### Returns\n\u003e \n\n---\n## add_expression\n#### Adds an expression to a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_expression(\n        name = 'DatabaseQuery',\n        expression = 'let...'\n    )\n```\n### Parameters\n\u003e **name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the expression.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The M-code encapsulating the logic for the expression.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the expression.\n\u003e\n### Returns\n\u003e \n\n---\n## add_field_parameter\n#### Adds a field parameter to a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_field_parameter(\n        table_name = 'Segment',\n        objects = [\"'Product'[Product Category]\", \"[Sales Amount]\", \"'Geography'[Country]\"]\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the field parameter.\n\u003e\n\u003e **objects** [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; A list of columns/tables to place in the field parameter. Columns must be fully qualified (i.e. \"'Table Name'[Column Name]\" and measures must be unqualified (i.e. \"[Measure Name]\").\n\u003e\n### Returns\n\u003e \n\n---\n## add_hierarchy\n#### Adds a hierarchy to a table within a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_hierarchy(\n        table_name = 'Geography',\n        hierarchy_name = 'Geo Hierarchy',\n        columns = ['Continent', 'Country', 'City']\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table in which the hierarchy will reside.\n\u003e\n\u003e **hierarchy_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the hierarchy.\n\u003e\n\u003e **columns** [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; A list of columns to use in the hierarchy. Must be ordered from the top of the hierarchy down (i.e. [\"Continent\", \"Country\", \"City\"]).\n\u003e\n\u003e **levels** [list](https://docs.python.org/3/library/stdtypes.html#list) of [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; A list of levels to use in the hierarchy. These will be the displayed name (instead of the column names). If omitted, the levels will default to showing the column names.\n\u003e\n\u003e **hierarchy_description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the hierarchy.\n\u003e\n\u003e **hierarchy_hidden** [bool](https://docs.python.org/3/library/stdtypes.html#bool)\n\u003e\n\u003e\u003e Optional; Sets the hierarchy to be hidden if True. Default value: False.\n\u003e\n### Returns\n\u003e\n\n---\n## add_m_partition\n#### Adds an M-partition to a table within a semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_m_partiiton(\n        table_name = 'Segment',\n        partition_name = 'Segment',\n        expression = 'let...',\n        mode = 'Import'\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table in which the partition will reside.\n\u003e\n\u003e **partition_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the M partition.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The M-code encapsulating the logic of the partition.\n\u003e\n\u003e **mode** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The storage mode for the partition. Default value: 'Import'.\n\u003e\n\u003e **description** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Optional; The description of the partition.\n\u003e\n### Returns\n\u003e \n\n---\n## add_measure\n#### Adds a measure to the semantic model.\n```python\nimport fabric_cat_tools as fct\nfrom fabric_cat_tools.TOM import connect_semantic_model\n\nwith connect_semantic_model(dataset = 'AdventureWorks', workspace = None, readonly = False) as tom:\n    tom.add_measure(\n        table_name = 'Sales',\n        measure_name = 'Sales Amount',\n        expression = \"SUM('Sales'[SalesAmount])\",\n        format_string = '$,00'\n    )\n```\n### Parameters\n\u003e **table_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the table in which the measure will reside.\n\u003e\n\u003e **measure_name** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The name of the measure.\n\u003e\n\u003e **expression** [str](https://docs.python.org/3/library/stdtypes.html#str)\n\u003e\n\u003e\u003e Required; The DAX expression encapsulati","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm-kovalsky%2Ffabric_cat_tools","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fm-kovalsky%2Ffabric_cat_tools","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm-kovalsky%2Ffabric_cat_tools/lists"}