fix(dc/tool-c): Fix special character handling and improve UX

Major fixes:
- Fix pivot transformation with special characters in column names
- Fix compute column validation for Chinese punctuation
- Fix recode dialog to fetch unique values from full dataset via new API
- Add column mapping mechanism to handle special characters

Database migration:
- Add column_mapping field to dc_tool_c_sessions table
- Migration file: 20251208_add_column_mapping

UX improvements:
- Darken table grid lines for better visibility
- Reduce column width by 40% with tooltip support
- Insert new columns next to source columns
- Preserve original row order after operations
- Add notice about 50-row preview limit

Modified files:
- Backend: SessionService, SessionController, QuickActionService, routes
- Python: pivot.py, compute.py, recode.py, binning.py, conditional.py
- Frontend: DataGrid, RecodeDialog, index.tsx, ag-grid-custom.css
- Database: schema.prisma, migration SQL

Status: Code complete, database migrated, ready for testing

extraction_service/operations/pivot.py

@@ -77,17 +77,39 @@ def pivot_long_to_wide(
             aggfunc=aggfunc
         )
         
-        # 展平多级列名
+        # ✨ 增强：展平多级列名（处理特殊字符）
         # 如果只有一个值列，列名是单层的
         if len(value_columns) == 1:
-            df_pivot.columns = [f'{value_columns[0]}_{col}' for col in df_pivot.columns]
+            # 清理列名中的特殊字符，使用安全的分隔符
+            value_col_clean = str(value_columns[0]).replace('(', '').replace(')', '').replace('=', '').strip()
+            df_pivot.columns = [f'{value_col_clean}___{str(col).replace(" ", "_")}' for col in df_pivot.columns]
         else:
             # 多个值列，列名是多层的，需要展平
-            df_pivot.columns = ['_'.join(str(c) for c in col).strip() for col in df_pivot.columns.values]
+            # 使用三个下划线作为分隔符（避免与列名中的下划线冲突）
+            new_columns = []
+            for col in df_pivot.columns.values:
+                if isinstance(col, tuple):
+                    # 清理每个部分的特殊字符
+                    parts = [str(c).replace('(', '').replace(')', '').replace('=', '').strip() for c in col]
+                    new_col = '___'.join(parts)
+                else:
+                    new_col = str(col).replace('(', '').replace(')', '').replace('=', '').strip()
+                new_columns.append(new_col)
+            df_pivot.columns = new_columns
         
         # 重置索引（将index列变回普通列）
         df_pivot = df_pivot.reset_index()
         
+        # ✨ 优化：保持原始行顺序（按照index_column排序）
+        # 获取原始数据中index_column的顺序
+        original_order = result[index_column].drop_duplicates().tolist()
+        # 创建排序映射
+        order_map = {val: idx for idx, val in enumerate(original_order)}
+        # 添加临时排序列
+        df_pivot['_sort_order'] = df_pivot[index_column].map(order_map)
+        # 按原始顺序排序
+        df_pivot = df_pivot.sort_values('_sort_order').drop(columns=['_sort_order']).reset_index(drop=True)
+        
         print(f'转换成功！')
         print(f'结果: {len(df_pivot)} 行 × {len(df_pivot.columns)} 列')
         print(f'新增列: {len(df_pivot.columns) - 1} 列')
@@ -159,3 +181,4 @@ def get_pivot_preview(
         'estimated_columns': len(unique_pivot)
     }
 
+