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在上一篇文章中,我们讨论了如何构建一个智能客服Agent。今天,我想分享另一个实际项目:如何构建一个数据分析Agent。这个项目源于我们一个金融客户的真实需求 - 提升数据分析效率,加快决策速度。

从分析师的痛点说起

记得和分析师团队交流时的场景：

小张：每天要分析这么多数据,真的很耗时
小李：是啊,而且经常要写各种分析报告
我：主要在哪些环节比较耗时？
小张：数据清洗、指标计算、图表生成这些都很繁琐
我：这些正好可以用AI Agent来协助

经过需求分析,我们确定了几个核心功能：

1. 智能数据清洗
2. 自动特征分析
3. 可视化生成
4. 报告撰写

技术方案设计

首先是整体架构：

from typing import List, Dict, Any, Optional
from enum import Enum
from pydantic import BaseModel
import pandas as pd
import numpy as npclass AnalysisTask(Enum):CLEAN = "clean"ANALYZE = "analyze"VISUALIZE = "visualize"REPORT = "report"class DataContext(BaseModel):data_path: strtask_type: AnalysisTaskrequirements: Dict[str, Any]history: List[Dict[str, Any]]class DataAnalyst:def __init__(self,config: Dict[str, Any]):# 1. 初始化分析模型self.analysis_model = AnalysisLLM(model="gpt-4",temperature=0.1,context_length=8000)# 2. 初始化工具集self.tools = {"cleaner": DataCleaner(),"analyzer": DataAnalyzer(),"visualizer": DataVisualizer(),"reporter": ReportGenerator()}# 3. 初始化数据存储self.data_store = DataStore(cache_dir="./cache",max_size_gb=10)async def process_task(self,context: DataContext) -> Dict[str, Any]:# 1. 加载数据data = await self._load_data(context.data_path)# 2. 理解需求requirements = await self._understand_requirements(context.requirements)# 3. 生成分析方案plan = await self._generate_plan(data,requirements)# 4. 执行分析result = await self._execute_analysis(data,plan)return resultasync def _understand_requirements(self,requirements: Dict[str, Any]) -> Dict[str, Any]:# 1. 提取分析目标objectives = await self.analysis_model.extract_objectives(requirements)# 2. 识别关键指标metrics = await self._identify_metrics(objectives)# 3. 确定分析方法methods = await self._select_methods(objectives,metrics)return {"objectives": objectives,"metrics": metrics,"methods": methods}

数据清洗功能

首先实现数据清洗功能：

class DataCleaner:def __init__(self,model: AnalysisLLM):self.model = modelasync def clean_data(self,data: pd.DataFrame) -> Dict[str, Any]:# 1. 数据概览profile = await self._profile_data(data)# 2. 识别问题issues = await self._identify_issues(data,profile)# 3. 执行清洗cleaned_data = await self._perform_cleaning(data,issues)return {"cleaned_data": cleaned_data,"profile": profile,"issues": issues}async def _identify_issues(self,data: pd.DataFrame,profile: Dict[str, Any]) -> List[Dict[str, Any]]:issues = []# 1. 检查缺失值missing = await self._check_missing_values(data)issues.extend(missing)# 2. 检查异常值outliers = await self._detect_outliers(data)issues.extend(outliers)# 3. 检查数据类型type_issues = await self._check_data_types(data)issues.extend(type_issues)return issuesasync def _perform_cleaning(self,data: pd.DataFrame,issues: List[Dict[str, Any]]) -> pd.DataFrame:cleaned = data.copy()for issue in issues:# 1. 处理缺失值if issue["type"] == "missing":cleaned = await self._handle_missing(cleaned,issue)# 2. 处理异常值elif issue["type"] == "outlier":cleaned = await self._handle_outlier(cleaned,issue)# 3. 处理类型问题elif issue["type"] == "type":cleaned = await self._handle_type(cleaned,issue)return cleaned

特征分析功能

接下来是特征分析功能：

class DataAnalyzer:def __init__(self,model: AnalysisLLM):self.model = modelasync def analyze_features(self,data: pd.DataFrame,requirements: Dict[str, Any]) -> Dict[str, Any]:# 1. 统计分析stats = await self._statistical_analysis(data)# 2. 特征相关性correlations = await self._correlation_analysis(data)# 3. 时间趋势trends = await self._trend_analysis(data)return {"statistics": stats,"correlations": correlations,"trends": trends}async def _statistical_analysis(self,data: pd.DataFrame) -> Dict[str, Any]:stats = {}# 1. 基础统计量basic_stats = await self._calculate_basic_stats(data)stats["basic"] = basic_stats# 2. 分布分析distribution = await self._analyze_distribution(data)stats["distribution"] = distribution# 3. 分组统计groupby = await self._group_statistics(data)stats["groupby"] = groupbyreturn statsasync def _correlation_analysis(self,data: pd.DataFrame) -> Dict[str, Any]:# 1. 计算相关系数corr_matrix = await self._calculate_correlations(data)# 2. 特征重要性importance = await self._feature_importance(data)# 3. 共线性检测collinearity = await self._check_collinearity(data)return {"correlation_matrix": corr_matrix,"feature_importance": importance,"collinearity": collinearity}

可视化功能

再来实现可视化功能：

class DataVisualizer:def __init__(self,model: AnalysisLLM):self.model = modelasync def create_visualizations(self,data: pd.DataFrame,analysis: Dict[str, Any]) -> Dict[str, Any]:# 1. 选择图表类型chart_types = await self._select_charts(data,analysis)# 2. 生成图表charts = await self._generate_charts(data,chart_types)# 3. 优化展示optimized = await self._optimize_display(charts)return {"charts": charts,"layout": optimized}async def _select_charts(self,data: pd.DataFrame,analysis: Dict[str, Any]) -> List[Dict[str, Any]]:charts = []# 1. 分布图表distribution_charts = await self._distribution_charts(data,analysis)charts.extend(distribution_charts)# 2. 关系图表relationship_charts = await self._relationship_charts(data,analysis)charts.extend(relationship_charts)# 3. 趋势图表trend_charts = await self._trend_charts(data,analysis)charts.extend(trend_charts)return chartsasync def _generate_charts(self,data: pd.DataFrame,chart_types: List[Dict[str, Any]]) -> List[Dict[str, Any]]:charts = []for chart_type in chart_types:# 1. 准备数据plot_data = await self._prepare_plot_data(data,chart_type)# 2. 设置样式style = await self._set_chart_style(chart_type)# 3. 生成图表chart = await self._plot_chart(plot_data,chart_type,style)charts.append({"type": chart_type,"data": plot_data,"style": style,"chart": chart})return charts

报告生成功能

最后是报告生成功能：

class ReportGenerator:def __init__(self,model: AnalysisLLM):self.model = modelasync def generate_report(self,data: pd.DataFrame,analysis: Dict[str, Any],visualizations: Dict[str, Any]) -> Dict[str, Any]:# 1. 提取要点key_points = await self._extract_key_points(analysis)# 2. 生成结构structure = await self._create_structure(key_points)# 3. 撰写内容content = await self._write_content(structure,analysis,visualizations)return {"key_points": key_points,"structure": structure,"content": content}async def _extract_key_points(self,analysis: Dict[str, Any]) -> List[Dict[str, Any]]:points = []# 1. 统计发现statistical_points = await self._extract_statistical_points(analysis["statistics"])points.extend(statistical_points)# 2. 相关性发现correlation_points = await self._extract_correlation_points(analysis["correlations"])points.extend(correlation_points)# 3. 趋势发现trend_points = await self._extract_trend_points(analysis["trends"])points.extend(trend_points)return pointsasync def _write_content(self,structure: Dict[str, Any],analysis: Dict[str, Any],visualizations: Dict[str, Any]) -> Dict[str, str]:content = {}# 1. 写摘要content["summary"] = await self._write_summary(structure,analysis)# 2. 写主体content["body"] = await self._write_body(structure,analysis,visualizations)# 3. 写结论content["conclusion"] = await self._write_conclusion(structure,analysis)return content

实际效果

经过两个月的使用,这个数据分析Agent带来了显著的效率提升：

1. 时间节省

   • 数据清洗时间减少70%
   • 分析流程加快50%
   • 报告生成效率提升60%

2. 质量提升

   • 分析更全面
   • 图表更专业
   • 报告更规范

3. 能力扩展

   • 支持更多数据源
   • 分析方法更丰富
   • 可视化更灵活

实践心得

在开发这个数据分析Agent的过程中,我总结了几点经验：

1. 需求导向

   • 理解分析目标
   • 把握重点指标
   • 注重实用性

2. 方法系统

   • 分析方法要系统
   • 工具选择要合理
   • 流程设计要清晰

3. 结果可用

   • 结论要有洞见
   • 图表要易理解
   • 报告要实用

写在最后

一个好的数据分析Agent不仅要会算数据,更要懂业务含义,能够帮助用户发现数据背后的价值。它就像一个经验丰富的数据分析师,在合适的时候给出恰当的分析建议。

在下一篇文章中,我会讲解如何开发一个文档助手Agent。如果你对数据分析Agent的开发有什么想法,欢迎在评论区交流。