Add error handling for plot part.

Author: MarenBermudezBoeckle

src/project_mbb/analysis/desc_analysis.py

@@ -133,9 +133,9 @@ def _fail_if_no_total_communes(enusc_clean, commune_mapping):
 
 def _fail_if_ses_not_categorical(enusc_clean, ses_column="socioecon_status"):
     """Raise a TypeError if 'socioecon_status' is not categorical."""
-    if not pd.api.types.is_categorical_dtype(enusc_clean[ses_column]):
+    if not isinstance(enusc_clean[ses_column].dtype, pd.CategoricalDtype):
         error_msg = (
-            f"Column '{ses_column}' must be of type 'category',"
+            f"Column '{ses_column}' must be of type 'category', "
             f"but got {enusc_clean[ses_column].dtype}."
         )
         raise TypeError(error_msg)

src/project_mbb/final/plot.py

@@ -11,6 +11,9 @@
 
 
 def plot_crime_perception(enusc_clean):
+    _fail_if_only_missing_values(enusc_clean)
+    _fail_if_missing_or_empty_columns(enusc_clean)
+
     crime_columns = {
         "crime_increase_perception_nation": "Crime Perception - Nation",
         "crime_increase_perception_commune": "Crime Perception - Commune",
@@ -45,3 +48,33 @@ def plot_crime_perception(enusc_clean):
         )
 
         fig.write_image(i)
+
+
+# Error Handling
+
+
+def _fail_if_missing_or_empty_columns(enusc_clean):
+    """Raises an error if required columns are missing."""
+    required_columns = {
+        "crime_increase_perception_nation",
+        "crime_increase_perception_commune",
+        "crime_increase_perception_neighborhood",
+    }
+
+    missing_columns = required_columns - set(enusc_clean.columns)
+    if missing_columns:
+        error_msg = f"Missing required columns: {missing_columns}"
+        raise ValueError(error_msg)
+
+
+def _fail_if_only_missing_values(enusc_clean):
+    """Raises an error if required columns contain only missing values."""
+    required_columns = {
+        "crime_increase_perception_nation",
+        "crime_increase_perception_commune",
+        "crime_increase_perception_neighborhood",
+    }
+    empty_columns = {col for col in required_columns if enusc_clean[col].dropna().empty}
+    if empty_columns:
+        error_msg = f"Columns contain only missing values: {empty_columns}"
+        raise ValueError(error_msg)