feat(dc/tool-c): Add pivot column ordering and NA handling features

Major features:
1. Pivot transformation enhancements:
   - Add option to keep unselected columns with 3 aggregation methods
   - Maintain original column order after pivot (aligned with source file)
   - Preserve pivot value order (first appearance order)

2. NA handling across 4 core functions:
   - Recode: Support keep/map/drop for NA values
   - Filter: Already supports is_null/not_null operators
   - Binning: Support keep/label/assign for NA values (fix nan display)
   - Conditional: Add is_null/not_null operators

3. UI improvements:
   - Enable column header tooltips with custom header component
   - Add closeable alert for 50-row preview
   - Fix page scrollbar issues

Modified files:
Python: pivot.py, recode.py, binning.py, conditional.py, main.py
Backend: SessionController, QuickActionController, QuickActionService
Frontend: PivotDialog, RecodeDialog, BinningDialog, ConditionalDialog, DataGrid, index

Status: Ready for testing

extraction_service/operations/binning.py

@@ -17,7 +17,10 @@ def apply_binning(
     new_column_name: str,
     bins: Optional[List[Union[int, float]]] = None,
     labels: Optional[List[Union[str, int]]] = None,
-    num_bins: int = 3
+    num_bins: int = 3,
+    na_handling: Literal['keep', 'label', 'assign'] = 'keep',
+    na_label: Optional[str] = None,
+    na_assign_to: Optional[int] = None
 ) -> pd.DataFrame:
     """
     应用分箱操作
@@ -33,16 +36,23 @@ def apply_binning(
         bins: 自定义切点列表（仅method='custom'时使用），如 [18, 60] → <18, 18-60, >60
         labels: 标签列表（可选）
         num_bins: 分组数量（仅method='equal_width'或'equal_freq'时使用）
+        na_handling: NA值处理方式
+            - 'keep': 保持为NA（默认）
+            - 'label': 标记为指定标签
+            - 'assign': 分配到指定组
+        na_label: 当na_handling='label'时，NA的标签（如"缺失"）
+        na_assign_to: 当na_handling='assign'时，NA分配到的组索引
     
     Returns:
         分箱后的数据框
     
     Examples:
-        >>> df = pd.DataFrame({'年龄': [15, 25, 35, 45, 55, 65, 75]})
+        >>> df = pd.DataFrame({'年龄': [15, 25, 35, 45, 55, 65, 75, None]})
         >>> result = apply_binning(df, '年龄', 'custom', '年龄分组', 
-        ...                        bins=[18, 60], labels=['青少年', '成年', '老年'])
+        ...                        bins=[18, 60], labels=['青少年', '成年', '老年'],
+        ...                        na_handling='label', na_label='缺失')
         >>> result['年龄分组'].tolist()
-        ['青少年', '成年', '成年', '成年', '成年', '老年', '老年']
+        ['青少年', '成年', '成年', '成年', '成年', '老年', '老年', '缺失']
     """
     if df.empty:
         return df
@@ -54,6 +64,10 @@ def apply_binning(
     # 创建结果数据框
     result = df.copy()
     
+    # ✨ 记录原始NA的位置（在分箱前）
+    original_na_mask = result[column].isna()
+    original_na_count = original_na_mask.sum()
+    
     # 验证并转换数据类型
     if not pd.api.types.is_numeric_dtype(result[column]):
         # 尝试将字符串转换为数值
@@ -136,6 +150,9 @@ def apply_binning(
     else:
         raise ValueError(f"不支持的分箱方法: {method}")
     
+    # ✨ 重要：将Categorical类型转换为object类型，避免"nan"字符串问题
+    result[new_column_name] = result[new_column_name].astype('object')
+    
     # ✨ 优化：将新列移到原列旁边
     original_col_index = result.columns.get_loc(column)
     cols = list(result.columns)
@@ -145,6 +162,27 @@ def apply_binning(
     cols.insert(original_col_index + 1, new_column_name)
     result = result[cols]
     
+    # ✨ 处理NA值（使用分箱前记录的NA位置）
+    if original_na_count > 0:
+        if na_handling == 'keep':
+            # 保持为NA（显式设置为None，避免显示为"nan"字符串）
+            result.loc[original_na_mask, new_column_name] = None
+            print(f'📊 NA处理：保持为NA（{original_na_count}个）', flush=True)
+        
+        elif na_handling == 'label':
+            # 标记为指定标签
+            label_to_use = na_label if na_label else '空值/NA'
+            result.loc[original_na_mask, new_column_name] = label_to_use
+            print(f'📊 NA处理：标记为 "{label_to_use}"（{original_na_count}个）', flush=True)
+        
+        elif na_handling == 'assign':
+            # 分配到指定组（通过labels）
+            if labels and na_assign_to is not None and 0 <= na_assign_to < len(labels):
+                result.loc[original_na_mask, new_column_name] = labels[na_assign_to]
+                print(f'📊 NA处理：分配到组 "{labels[na_assign_to]}"（{original_na_count}个）', flush=True)
+            else:
+                print(f'⚠️  警告：na_assign_to无效，NA保持为空', flush=True)
+    
     # 统计分布
     print(f'分箱结果分布:')
     value_counts = result[new_column_name].value_counts().sort_index()