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/main.py

@@ -97,6 +97,8 @@ class RecodeRequest(BaseModel):
     mapping: Dict[Any, Any]
     create_new_column: bool = True
     new_column_name: str = None
+    na_handling: str = 'keep'  # ✨ 新增：NA处理方式（keep/map/drop）
+    na_value: Any = None  # ✨ 新增：NA映射值
 
 class BinningRequest(BaseModel):
     """分箱请求模型"""
@@ -107,6 +109,9 @@ class BinningRequest(BaseModel):
     bins: List[Any] = None
     labels: List[Any] = None
     num_bins: int = 3
+    na_handling: str = 'keep'  # ✨ 新增：NA处理方式（keep/label/assign）
+    na_label: str = None  # ✨ 新增：NA标签
+    na_assign_to: int = None  # ✨ 新增：NA分配到的组索引
 
 class ConditionalRequest(BaseModel):
     """条件生成列请求模型"""
@@ -127,6 +132,7 @@ class ComputeRequest(BaseModel):
     data: List[Dict[str, Any]]
     new_column_name: str
     formula: str
+    column_mapping: List[Dict[str, str]] = []  # ✨ 新增：列名映射
 
 class PivotRequest(BaseModel):
     """Pivot请求模型"""
@@ -135,6 +141,11 @@ class PivotRequest(BaseModel):
     pivot_column: str
     value_columns: List[str]
     aggfunc: str = 'first'
+    column_mapping: List[Dict[str, str]] = []  # ✨ 列名映射
+    keep_unused_columns: bool = False  # ✨ 是否保留未选择的列
+    unused_agg_method: str = 'first'  # ✨ 未选择列的聚合方式（first/mode/mean）
+    original_column_order: List[str] = []  # ✨ 新增：原始列顺序
+    pivot_value_order: List[str] = []  # ✨ 新增：透视列值的原始顺序
 
 
 # ==================== API路由 ====================
@@ -763,13 +774,15 @@ async def operation_recode(request: RecodeRequest):
             # 转换为DataFrame
             df = pd.DataFrame(request.data)
             
-            # 调用预写函数
+            # 调用预写函数（传递NA处理参数）
             result_df = apply_recode(
                 df,
                 request.column,
                 request.mapping,
                 request.create_new_column,
-                request.new_column_name
+                request.new_column_name,
+                request.na_handling,  # ✨ NA处理方式
+                request.na_value  # ✨ NA映射值
             )
             
             # 转换回JSON（处理NaN和inf值）
@@ -840,7 +853,7 @@ async def operation_binning(request: BinningRequest):
             # 转换为DataFrame
             df = pd.DataFrame(request.data)
             
-            # 调用预写函数
+            # 调用预写函数（传递NA处理参数）
             result_df = apply_binning(
                 df,
                 request.column,
@@ -848,7 +861,10 @@ async def operation_binning(request: BinningRequest):
                 request.new_column_name,
                 request.bins,
                 request.labels,
-                request.num_bins
+                request.num_bins,
+                request.na_handling,  # ✨ NA处理方式
+                request.na_label,  # ✨ NA标签
+                request.na_assign_to  # ✨ NA分配到的组索引
             )
             
             # 转换回JSON（处理Categorical类型、NaN值和inf值）
@@ -1106,11 +1122,12 @@ async def operation_compute(request: ComputeRequest):
             # 转换为DataFrame
             df = pd.DataFrame(request.data)
             
-            # 调用预写函数
+            # ✨ 调用预写函数（传递column_mapping）
             result_df = compute_column(
                 df,
                 request.new_column_name,
-                request.formula
+                request.formula,
+                request.column_mapping  # ✨ 传递列名映射
             )
             
             # 转换回JSON（处理NaN值和inf值）
@@ -1201,13 +1218,18 @@ async def operation_pivot(request: PivotRequest):
             # 转换为DataFrame
             df = pd.DataFrame(request.data)
             
-            # 调用预写函数
+            # ✨ 调用预写函数（传递column_mapping和未选择列处理参数）
             result_df = pivot_long_to_wide(
                 df,
                 request.index_column,
                 request.pivot_column,
                 request.value_columns,
-                request.aggfunc
+                request.aggfunc,
+                request.column_mapping,  # ✨ 传递列名映射
+                request.keep_unused_columns,  # ✨ 是否保留未选择的列
+                request.unused_agg_method,  # ✨ 未选择列的聚合方式
+                request.original_column_order,  # ✨ 原始列顺序
+                request.pivot_value_order  # ✨ 透视列值的原始顺序
             )
             
             # 转换回JSON（处理NaN和inf值）