feat(dc): Complete Tool C quick action buttons Phase 1-2 - 7 functions

Summary:
- Implement 7 quick action functions (filter, recode, binning, conditional, dropna, compute, pivot)
- Refactor to pre-written Python functions architecture (stable and secure)
- Add 7 Python operations modules with full type hints
- Add 7 frontend Dialog components with user-friendly UI
- Fix NaN serialization issues and auto type conversion
- Update all related documentation

Technical Details:
- Python: operations/ module (filter.py, recode.py, binning.py, conditional.py, dropna.py, compute.py, pivot.py)
- Backend: QuickActionService.ts with 7 execute methods
- Frontend: 7 Dialog components with complete validation
- Toolbar: Enable 7 quick action buttons

Status: Phase 1-2 completed, basic testing passed, ready for further testing

python-microservice/operations/filter.py

@@ -0,0 +1,109 @@
+"""
+高级筛选操作
+
+提供多条件筛选功能，支持AND/OR逻辑组合。
+"""
+
+import pandas as pd
+from typing import List, Dict, Any, Literal
+
+
+def apply_filter(
+    df: pd.DataFrame,
+    conditions: List[Dict[str, Any]],
+    logic: Literal['and', 'or'] = 'and'
+) -> pd.DataFrame:
+    """
+    应用筛选条件
+    
+    Args:
+        df: 输入数据框
+        conditions: 筛选条件列表，每个条件包含：
+            - column: 列名
+            - operator: 运算符 (=, !=, >, <, >=, <=, contains, not_contains, 
+                       starts_with, ends_with, is_null, not_null)
+            - value: 值（is_null和not_null不需要）
+        logic: 逻辑组合方式 ('and' 或 'or')
+    
+    Returns:
+        筛选后的数据框
+    
+    Examples:
+        >>> df = pd.DataFrame({'年龄': [25, 35, 45], '性别': ['男', '女', '男']})
+        >>> conditions = [
+        ...     {'column': '年龄', 'operator': '>', 'value': 30},
+        ...     {'column': '性别', 'operator': '=', 'value': '男'}
+        ... ]
+        >>> result = apply_filter(df, conditions, logic='and')
+        >>> len(result)
+        1
+    """
+    if not conditions:
+        raise ValueError('筛选条件不能为空')
+    
+    if df.empty:
+        return df
+    
+    # 生成各个条件的mask
+    masks = []
+    for cond in conditions:
+        column = cond['column']
+        operator = cond['operator']
+        value = cond.get('value')
+        
+        # 验证列是否存在
+        if column not in df.columns:
+            raise KeyError(f"列 '{column}' 不存在")
+        
+        # 根据运算符生成mask
+        if operator == '=':
+            mask = df[column] == value
+        elif operator == '!=':
+            mask = df[column] != value
+        elif operator == '>':
+            mask = df[column] > value
+        elif operator == '<':
+            mask = df[column] < value
+        elif operator == '>=':
+            mask = df[column] >= value
+        elif operator == '<=':
+            mask = df[column] <= value
+        elif operator == 'contains':
+            mask = df[column].astype(str).str.contains(str(value), na=False)
+        elif operator == 'not_contains':
+            mask = ~df[column].astype(str).str.contains(str(value), na=False)
+        elif operator == 'starts_with':
+            mask = df[column].astype(str).str.startswith(str(value), na=False)
+        elif operator == 'ends_with':
+            mask = df[column].astype(str).str.endswith(str(value), na=False)
+        elif operator == 'is_null':
+            mask = df[column].isna()
+        elif operator == 'not_null':
+            mask = df[column].notna()
+        else:
+            raise ValueError(f"不支持的运算符: {operator}")
+        
+        masks.append(mask)
+    
+    # 组合所有条件
+    if logic == 'and':
+        final_mask = pd.concat(masks, axis=1).all(axis=1)
+    elif logic == 'or':
+        final_mask = pd.concat(masks, axis=1).any(axis=1)
+    else:
+        raise ValueError(f"不支持的逻辑运算: {logic}")
+    
+    # 应用筛选
+    result = df[final_mask].copy()
+    
+    # 打印统计信息
+    original_rows = len(df)
+    filtered_rows = len(result)
+    removed_rows = original_rows - filtered_rows
+    
+    print(f'原始数据: {original_rows} 行')
+    print(f'筛选后: {filtered_rows} 行')
+    print(f'删除: {removed_rows} 行 ({removed_rows/original_rows*100:.1f}%)')
+    
+    return result
+