fabric_cat_tools 0.3.1

• Microsoft updated the column names in the table showing Direct Lake's guardrails. Updated dependent functions (#4).
• The vertipaq_analyzer function now supports executing against semantic models in different workspaces and if the lakehouse used for Direct Lake is hosted in a different workspace (by using the lakehouse_workspace parameter).
• Added the create_warehouse function which, as expected, creates a new Fabric warehouse.
• Added the update_item function. This allows you to update the name/description of any of the following Fabric items: 'DataPipeline', 'Eventstream', 'KQLDatabase', 'KQLQueryset', 'Lakehouse', 'MLExperiment', 'MLModel', 'Notebook', 'Warehouse'.
• Updated export_report to check that a lakehouse is attached to the notebook.
• Added the report_filter parameter to the export_report function, enabling a report filter to be specified upon exporting the report to a file. See the examples here as the syntax has been made user friendly.
• Added the 'return_dataframe' parameter to the run_model_bpa function which returns a simple pandas dataframe with the BPA results instead of the standard visualization.
• Added the 'export' parameter to the run_model_bpa function which allows for exporting the model BPA results to a table in your lakehouse.
• The export capability in the vertipaq_analyzer function and the run_model_bpa function exports columns for 'Timestamp' and 'RunId' (RunId increments with each export run so it is easy to track and filter.