Delete 'toolorchestratoolsdata_processor.py'

toolorchestratoolsdata_processor.py

@@ -1,87 +0,0 @@
-"""
-数据处理工具示例
-符合ToolInterface接口规范
-"""
-from typing import Dict, Any
-import pandas as pd
-import numpy as np
-from ..core.orchestrator import ToolInterface, LogMixin
-
-class DataProcessorTool(ToolInterface, LogMixin):
-    """数据处理工具"""
-    
-    def __init__(self):
-        self._name = "data_processor"
-        self._version = "1.0.0"
-        
-    @property
-    def name(self) -> str:
-        return self._name
-        
-    @property
-    def version(self) -> str:
-        return self._version
-        
-    def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
-        """执行数据处理"""
-        try:
-            self.logger.info(f"开始数据处理，参数: {params}")
-            
-            # 输入验证
-            data = params.get("data", [])
-            operation = params.get("operation", "clean")
-            
-            if not data:
-                raise ValueError("输入数据为空")
-                
-            # 转换为DataFrame
-            df = pd.DataFrame(data)
-            
-            # 执行不同操作
-            if operation == "clean":
-                result = self._clean_data(df)
-            elif operation == "analyze":
-                result = self._analyze_data(df)
-            elif operation == "transform":
-                result = self._transform_data(df)
-            else:
-                raise ValueError(f"不支持的操作: {operation}")
-                
-            self.logger.info("数据处理完成")
-            return {
-                "status": "success",
-                "operation": operation,
-                "result": result,
-                "rows_processed": len(df)
-            }
-            
-        except Exception as e:
-            self.logger.error(f"数据处理失败: {str(e)}")
-            raise
-            
-    def _clean_data(self, df: pd.DataFrame) -> Dict:
-        """数据清洗"""
-        # 移除空值
-        df_clean = df.dropna()
-        # 重置索引
-        df_clean = df_clean.reset_index(drop=True)
-        
-        return {
-            "cleaned_data": df_clean.to_dict(orient="records"),
-            "removed_rows": len(df) - len(df_clean)
-        }
-        
-    def _analyze_data(self, df: pd.DataFrame) -> Dict:
-        """数据分析"""
-        numeric_cols = df.select_dtypes(include=[np.number]).columns
-        
-        analysis = {}
-        for col in numeric_cols:
-            analysis[col] = {
-                "mean": float(df[col].mean()),
-                "std": float(df[col].std()),
-                "min": float(df[col].min()),
-                "max": float(df[col].max())
-            }
-            
-        return {"analysis": analysis}