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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：
- 智能数据清洗
- 自动特征分析
- 可视化生成
- 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：
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%
质量提升
- 分析更全面
- 图表更专业
- 报告更规范
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：
需求导向
- 理解分析目标
- 把握重点指标
- 注重实用性
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
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。