feat([2fd88f42]): display attributes in point tooltip

heatmap-tool/backend/main.py

@@ -116,8 +116,24 @@ async def get_heatmap_data(request: FilterRequest):
         if filtered_df.empty:
             return []
 
-        # Aggregate data by PLZ
-        plz_counts = filtered_df.groupby(plz_column_name).size().reset_index(name='count')
+        # Aggregate data by PLZ, and also collect attribute summaries
+        plz_grouped = filtered_df.groupby(plz_column_name)
+        plz_counts = plz_grouped.size().reset_index(name='count')
+
+        # Collect unique attributes for each PLZ
+        attribute_summaries = {}
+        for plz_val, group in plz_grouped:
+            summary = {}
+            for col in filtered_df.columns:
+                if col != plz_column_name and col != 'lat' and col != 'lon': # Exclude lat/lon if they somehow exist
+                    unique_attrs = group[col].unique().tolist()
+                    # Limit to top 3 unique values for readability
+                    summary[col] = unique_attrs[:3]
+            attribute_summaries[plz_val] = summary
+
+        # Convert summaries to a DataFrame for merging
+        summary_df = pd.DataFrame.from_dict(attribute_summaries, orient='index')
+        summary_df.index.name = plz_column_name
 
         # --- Geocoding Step ---
         # Merge the aggregated counts with the geocoding dataframe
@@ -129,17 +145,45 @@ async def get_heatmap_data(request: FilterRequest):
             how='inner'
         )
         
+        # Merge with attribute summaries
+        merged_df = pd.merge(
+            merged_df,
+            summary_df,
+            left_on=plz_column_name,
+            right_index=True,
+            how='left'
+        )
+
         # Rename columns to match frontend expectations ('lon' and 'lat')
         merged_df.rename(columns={'x': 'lon', 'y': 'lat'}, inplace=True)
         
         # Also rename the original PLZ column to the consistent name 'plz'
         merged_df.rename(columns={plz_column_name: 'plz'}, inplace=True)
         
-        # Convert to the required JSON format
-        heatmap_data = merged_df[['plz', 'lat', 'lon', 'count']].to_dict(orient='records')
+        # Convert to the required JSON format, including all remaining columns (which are the attributes)
+        # We'll dynamically collect attribute columns for output
+        output_columns = ['plz', 'lat', 'lon', 'count']
+        for col in merged_df.columns:
+            if col not in output_columns and col != plz_column_name: # Ensure we don't duplicate PLZ or coords
+                output_columns.append(col)
+
+        heatmap_data = merged_df[output_columns].to_dict(orient='records')
         
-        print(f"Generated heatmap data with {len(heatmap_data)} PLZ points.")
-        return heatmap_data
+        # The frontend expects 'attributes_summary' as a single field, so let's restructure for that
+        # For each record, pick out the attributes that are not 'plz', 'lat', 'lon', 'count'
+        final_heatmap_data = []
+        for record in heatmap_data:
+            attrs = {k: v for k, v in record.items() if k not in ['plz', 'lat', 'lon', 'count']}
+            final_heatmap_data.append({
+                "plz": record['plz'],
+                "lat": record['lat'],
+                "lon": record['lon'],
+                "count": record['count'],
+                "attributes_summary": attrs
+            })
+
+        print(f"Generated heatmap data with {len(final_heatmap_data)} PLZ points.")
+        return final_heatmap_data
 
     except Exception as e:
         print(f"ERROR generating heatmap: {e}")