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/recode.py

@@ -0,0 +1,79 @@
+"""
+数值映射（重编码）操作
+
+将分类变量的原始值映射为新值（如：男→1，女→2）。
+"""
+
+import pandas as pd
+from typing import Dict, Any, Optional
+
+
+def apply_recode(
+    df: pd.DataFrame,
+    column: str,
+    mapping: Dict[Any, Any],
+    create_new_column: bool = True,
+    new_column_name: Optional[str] = None
+) -> pd.DataFrame:
+    """
+    应用数值映射
+    
+    Args:
+        df: 输入数据框
+        column: 要重编码的列名
+        mapping: 映射字典，如 {'男': 1, '女': 2}
+        create_new_column: 是否创建新列（True）或覆盖原列（False）
+        new_column_name: 新列名（create_new_column=True时使用）
+    
+    Returns:
+        重编码后的数据框
+    
+    Examples:
+        >>> df = pd.DataFrame({'性别': ['男', '女', '男', '女']})
+        >>> mapping = {'男': 1, '女': 2}
+        >>> result = apply_recode(df, '性别', mapping, True, '性别_编码')
+        >>> result['性别_编码'].tolist()
+        [1, 2, 1, 2]
+    """
+    if df.empty:
+        return df
+    
+    # 验证列是否存在
+    if column not in df.columns:
+        raise KeyError(f"列 '{column}' 不存在")
+    
+    if not mapping:
+        raise ValueError('映射字典不能为空')
+    
+    # 确定目标列名
+    if create_new_column:
+        target_column = new_column_name or f'{column}_编码'
+    else:
+        target_column = column
+    
+    # 创建结果数据框（避免修改原数据）
+    result = df.copy()
+    
+    # 应用映射
+    result[target_column] = result[column].map(mapping)
+    
+    # 统计结果
+    mapped_count = result[target_column].notna().sum()
+    unmapped_count = result[target_column].isna().sum()
+    total_count = len(result)
+    
+    print(f'映射完成: {mapped_count} 个值成功映射')
+    
+    if unmapped_count > 0:
+        print(f'警告: {unmapped_count} 个值未找到对应映射')
+        # 找出未映射的唯一值
+        unmapped_mask = result[target_column].isna()
+        unmapped_values = result.loc[unmapped_mask, column].unique()
+        print(f'未映射的值: {list(unmapped_values)[:10]}')  # 最多显示10个
+    
+    # 映射成功率
+    success_rate = (mapped_count / total_count * 100) if total_count > 0 else 0
+    print(f'映射成功率: {success_rate:.1f}%')
+    
+    return result
+