added list_semantic_model_errors

src/sempy_labs/__init__.py

@@ -205,6 +205,7 @@
     list_lakehouses,
     list_sql_endpoints,
     update_item,
+    list_semantic_model_errors,
 )
 from sempy_labs._helper_functions import (
     convert_to_friendly_case,
@@ -458,4 +459,5 @@
     "update_vnet_gateway",
     "update_on_premises_gateway",
     "get_semantic_model_definition",
+    "list_semantic_model_errors",
 ]

src/sempy_labs/_list_functions.py

@@ -1575,3 +1575,72 @@ def list_semantic_model_object_report_usage(
     final_df.reset_index(drop=True, inplace=True)
 
     return final_df
+
+def list_semantic_model_errors(dataset: str | UUID, workspace: Optional[str | UUID]) -> pd.DataFrame:
+    """
+    Shows a list of a semantic model's errors and their error messages (if they exist).
+
+    Parameters
+    ----------
+    dataset : str | UUID
+        Name or ID of the semantic model.
+    workspace : str | UUID, default=None
+        The Fabric workspace name or ID.
+        Defaults to None which resolves to the workspace of the attached lakehouse
+        or if no lakehouse attached, resolves to the workspace of the notebook.
+
+    Returns
+    -------
+    pandas.DataFrame
+        A pandas dataframe showing a list of the errors and error messages for a given semantic model.
+    """
+
+    from sempy_labs.tom import connect_semantic_model
+
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+    (dataset_name, dataset_id) = resolve_dataset_name_and_id(
+        dataset, workspace=workspace_id
+    )
+
+    error_rows = []
+
+    # Helper function to add a new error row
+    def add_error_row(object_type, table_name, object_name, error_message):
+        error_rows.append({
+            "Object Type": object_type,
+            "Table Name": table_name,
+            "Object Name": object_name,
+            "Error Message": error_message,
+        })
+
+    with connect_semantic_model(dataset=dataset_id, workspace=workspace_id, readonly=True) as tom:
+        # Define mappings of TOM objects to object types and attributes
+        error_checks = [
+            ("Column", tom.all_columns, lambda o: o.ErrorMessage),
+            ("Partition", tom.all_partitions, lambda o: o.ErrorMessage),
+            ("Partition - Data Coverage Expression", 
+                tom.all_partitions, lambda o: o.DataCoverageDefinition.ErrorMessage if o.DataCoverageDefinition else ""),
+            ("Row Level Security", tom.all_rls, lambda o: o.ErrorMessage),
+            ("Calculation Item", tom.all_calculation_items, lambda o: o.ErrorMessage),
+            ("Measure", tom.all_measures, lambda o: o.ErrorMessage),
+            ("Measure - Detail Rows Expression", 
+                tom.all_measures, lambda o: o.DetailRowsDefinition.ErrorMessage if o.DetailRowsDefinition else ""),
+            ("Measure - Format String Expression", 
+                tom.all_measures, lambda o: o.FormatStringDefinition.ErrorMessage if o.FormatStringDefinition else ""),
+            ("Calculation Group - Multiple or Empty Selection Expression", 
+                tom.all_calculation_groups, lambda o: o.CalculationGroup.MultipleOrEmptySelectionExpression.ErrorMessage 
+                if o.CalculationGroup.MultipleOrEmptySelectionExpression else ""),
+            ("Calculation Group - No Selection Expression", 
+                tom.all_calculation_groups, lambda o: o.CalculationGroup.NoSelectionExpression.ErrorMessage 
+                if o.CalculationGroup.NoSelectionExpression else "")
+        ]
+
+        # Iterate over all error checks
+        for object_type, getter, error_extractor in error_checks:
+            for obj in getter():
+                error_message = error_extractor(obj)
+                if error_message:  # Only add rows if there's an error message
+                    add_error_row(object_type, obj.Parent.Name, obj.Name, error_message)
+
+    # Create the DataFrame from collected rows
+    return pd.DataFrame(error_rows)