Merge pull request #910 from minghaoxia61-web/feature/data-analyst

[毕业设计] DataAnalyst - 智能数据分析助手

Co-creation-projects/minghaoxia61-web-DataAnalyst/.env.example

@@ -0,0 +1,6 @@
+# 复制本文件为 .env 并填入你自己的配置（.env 已被 .gitignore 忽略，不会提交）
+# DeepSeek 示例：https://platform.deepseek.com 申请 API Key
+LLM_MODEL_ID=deepseek-chat
+LLM_API_KEY=your_api_key_here
+LLM_BASE_URL=https://api.deepseek.com/v1
+LLM_TIMEOUT=60

Co-creation-projects/minghaoxia61-web-DataAnalyst/.gitignore

@@ -0,0 +1,18 @@
+# 环境与密钥（切勿提交真实 API Key）
+.env
+.venv/
+venv/
+
+# Python 缓存
+__pycache__/
+*.py[cod]
+.ipynb_checkpoints/
+
+# HelloAgents 框架运行时生成的会话与轨迹文件
+memory/
+
+# 大文件与临时输出
+*.mp4
+*.zip
+*.tar.gz
+outputs/*.tmp

Co-creation-projects/minghaoxia61-web-DataAnalyst/README.md

@@ -0,0 +1,157 @@
+# DataAnalyst - 智能数据分析助手
+
+> 基于 HelloAgents 框架的三阶段多智能体数据分析流水线：**规划 → 分析 → 报告**，一键把任意 CSV 变成图文并茂的数据分析报告。
+
+## 📝 项目简介
+
+数据分析是业务决策的重要环节，但人工分析耗时长、容易遗漏数据中的关键模式，现有的 BI 工具又需要手工拖拽配置。DataAnalyst 基于多智能体协作思路解决这个问题：你只需要**替换一个 CSV 文件**，智能体团队就会自动完成数据探查、分析规划、多维度统计、图表生成和报告撰写。
+
+### 解决什么问题？
+
+- 数据分析门槛高：不懂 pandas / 统计的业务人员也能获得专业分析
+- 分析流程碎片化：探查、统计、绘图、写报告一步到位
+- 分析深度不足：智能体主动规划任务，主动挖掘趋势、结构、相关性与异常
+
+### 适用于什么场景？
+
+电商/零售订单分析、运营数据周报、任何"一份 CSV + 想知道里面有什么"的场景。
+
+## ✨ 核心功能
+
+- ✅ **三阶段多智能体流水线**：分析规划师（ReActAgent）规划任务 → 数据分析员（ReActAgent）逐任务调用工具深度分析 → 报告撰写师（SimpleAgent）汇总成文
+- ✅ **通用数据集支持**：任意 CSV 均可分析，字段自动识别，无需修改任何代码
+- ✅ **6 个原子分析工具**：数据概览、单列画像、相关性分析、分组聚合、IQR 异常检测、6 种统计图表绘制
+- ✅ **自动中文图表**：直方图/柱状图/箱线图/折线图/散点图/热力图，自动处理中文字体，保存 PNG 并嵌入报告
+- ✅ **稳健的工程细节**：规划输出「JSON 优先 + 正则兜底」双解析、工具层参数校验与自纠错、异常处理
+
+## 🛠️ 技术栈
+
+- **HelloAgents 框架**：`ReActAgent`（基于 Function Calling 的推理与行动）、`SimpleAgent`、`ToolRegistry`、`Tool/ToolResponse` 工具体系
+- **智能体范式**：Plan-and-Solve（三阶段任务分解）+ ReAct（工具调用循环）
+- **数据分析**：pandas（统计聚合）、matplotlib（可视化）
+- **LLM**：DeepSeek（`deepseek-chat`，OpenAI 兼容接口，任何兼容 API 均可通过 `.env` 切换）
+
+## 🚀 快速开始
+
+### 环境要求
+
+- Python 3.10+
+
+### 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+### 配置 API 密钥
+
+```bash
+# 1. 复制环境变量示例文件
+cp .env.example .env
+
+# 2. 编辑 .env，填入你的 DeepSeek API Key（https://platform.deepseek.com 申请）
+LLM_MODEL_ID=deepseek-chat
+LLM_API_KEY=your_api_key_here
+LLM_BASE_URL=https://api.deepseek.com/v1
+```
+
+> 使用其他 OpenAI 兼容 API（如 ModelScope、智谱）也可以，只需修改 `.env` 中的三个变量。
+
+### 运行项目
+
+```bash
+jupyter lab
+# 打开 main.ipynb，从上到下运行所有单元格
+```
+
+运行结束后查看结果：
+
+- 📄 分析报告：`outputs/analysis_report.md`
+- 📊 分析图表：`outputs/charts/*.png`
+
+## 📖 使用示例
+
+1. 把你的数据集放到 `data/` 目录（项目自带一份 800 条的模拟电商销售数据 `data/sales_data.csv`）
+2. 修改 Notebook 第 1 部分的 `DATA_PATH` 指向你的文件（默认即可）
+3. 运行 `main.ipynb` 全部单元格
+
+流水线运行过程（示例输出）：
+
+```
+============================================================
+【阶段1/3】分析规划：探查数据并生成分析任务
+✅ 规划完成，共 4 个分析任务:
+   任务1: 销售额月度趋势分析
+   任务2: 产品类别结构分析
+   任务3: 地区与渠道销售对比
+   任务4: 客户满意度影响因素与异常订单检测
+============================================================
+【阶段2/3】逐任务深度分析
+>>> 执行任务1: 销售额月度趋势分析
+...
+============================================================
+【阶段3/3】撰写分析报告
+✅ 报告已保存: outputs/analysis_report.md
+✅ 共生成图表 5 张，保存于 outputs/charts/
+```
+
+> 💡 没有配置 API 密钥？Notebook **第 7 部分「工具自检」**不消耗任何 LLM 调用，可直接运行体验工具层能力。
+
+### 分析自己想分析的数据？
+
+支持任意 CSV。例如把一份考试成绩表放到 `data/`，智能体会自动识别字段并围绕"分数分布、科目相关性、班级对比、异常值"等角度重新规划分析任务。
+
+## 📊 示例输出
+
+对自带电商数据集的一次完整分析（见 `outputs/` 目录）自动发现了：
+
+- 销售额的**大促季节性**：6 月、11 月、12 月为全年峰值
+- **品类集中度**：电子产品贡献约 75% 的销售额
+- **体验短板**：客户满意度与配送天数呈中等负相关（r ≈ -0.59）
+- **异常订单**：IQR 检出多笔疑似批发的异常大额订单
+
+## 🎯 项目亮点
+
+- **架构清晰**：规划/执行/撰写三阶段职责分离，范式对应教材第 4、7 章（Plan-and-Solve + ReAct）
+- **真正通用**：工具层只依赖数据本身，换数据集即换分析主题
+- **工程稳健**：双解析兜底、参数校验自纠错、图表文件名防冲突、中文渲染适配
+- **易于评审**：工具层可独立运行自检，报告与图表全部落盘可追溯
+
+## 📁 项目结构
+
+```
+minghaoxia61-web-DataAnalyst/
+├── README.md              # 项目说明文档
+├── requirements.txt       # Python 依赖列表
+├── main.ipynb             # 主程序：6个工具 + 三智能体流水线 + 完整演示
+├── .env.example           # 环境变量示例（复制为 .env 使用）
+├── .gitignore             # Git 忽略规则（防止泄露 API Key）
+├── data/
+│   └── sales_data.csv     # 模拟电商销售数据集（800条，85KB）
+└── outputs/               # 示例运行输出
+    ├── analysis_report.md # 分析报告示例
+    └── charts/            # 分析图表示例
+```
+
+## 🔮 未来计划
+
+- [ ] 支持 Excel 多 Sheet 与数据库数据源
+- [ ] 引入 ReflectionAgent 对报告质量自动复审
+- [ ] 增加 Gradio 交互界面，支持拖拽上传
+- [ ] 分析结果缓存与增量分析
+
+## 🤝 贡献指南
+
+欢迎提出 Issue 和 Pull Request！
+
+## 📄 许可证
+
+MIT License
+
+## 👤 作者
+
+- GitHub: [@minghaoxia61-web](https://github.com/minghaoxia61-web)
+
+## 🙏 致谢
+
+感谢 Datawhale 社区和 Hello-Agents 项目！

Co-creation-projects/minghaoxia61-web-DataAnalyst/data/sales_data.csv

@@ -0,0 +1,801 @@
+订单ID,订单日期,产品类别,产品名称,单价,数量,销售额,折扣率,地区,销售渠道,支付方式,客户满意度,配送天数
+ORD202400001,2024-11-23,电子产品,智能手表,1015.81,1,1015.81,0.0,华南,线下门店,支付宝,4.4,3
+ORD202400002,2024-04-11,美妆个护,防晒霜,92.71,1,88.07,0.05,西南,线上商城,微信支付,4.1,3
+ORD202400003,2024-02-17,家居用品,保温杯,71.78,2,143.56,0.0,华南,线上商城,货到付款,5.0,2
+ORD202400004,2024-10-22,家居用品,空气炸锅,328.22,1,311.81,0.05,西北,线下门店,银行卡,4.5,4
+ORD202400005,2024-12-09,家居用品,空气炸锅,392.44,1,372.82,0.05,华东,线上商城,货到付款,4.3,1
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+ORD202400063,2024-01-19,电子产品,笔记本电脑,5507.67,1,4956.9,0.1,华东,线下门店,支付宝,5.0,3
+ORD202400064,2024-05-15,家居用品,记忆枕,156.74,3,423.2,0.1,东北,直播带货,银行卡,4.1,4
+ORD202400065,2024-05-27,食品饮料,坚果礼盒,86.26,1,86.26,0.0,华北,直播带货,银行卡,4.4,4
+ORD202400066,2024-09-28,服装鞋帽,运动鞋,306.34,1,275.71,0.1,华南,线上商城,银行卡,4.5,4
+ORD202400067,2024-08-26,食品饮料,进口零食包,59.23,1,56.27,0.05,西北,线上商城,货到付款,3.6,4
+ORD202400068,2024-02-24,家居用品,台灯,86.19,1,81.88,0.05,华东,线下门店,微信支付,4.2,4
+ORD202400069,2024-09-11,食品饮料,红茶礼盒,119.1,1,113.14,0.05,华东,线上商城,支付宝,4.1,2
+ORD202400070,2024-11-17,美妆个护,香水,456.36,1,410.72,0.1,华南,线上商城,支付宝,4.0,3
+ORD202400071,2024-09-28,服装鞋帽,T恤,86.83,1,78.15,0.1,东北,线上商城,银行卡,3.6,4
+ORD202400072,2024-12-12,美妆个护,电动牙刷,262.24,3,708.05,0.1,华东,线上商城,支付宝,4.7,3
+ORD202400073,2024-11-22,食品饮料,坚果礼盒,92.62,1,74.1,0.2,华中,线上商城,银行卡,4.5,3
+ORD202400074,2024-07-28,电子产品,智能手机,3628.41,1,3265.57,0.1,华东,线上商城,微信支付,5.0,1
+ORD202400075,2024-10-27,美妆个护,洗发水,66.94,2,120.49,0.1,华南,线下门店,支付宝,4.2,3
+ORD202400076,2024-12-17,家居用品,保温杯,56.43,1,56.43,0.0,东北,线下门店,支付宝,4.6,4
+ORD202400077,2024-03-05,美妆个护,洗发水,52.23,2,104.46,0.0,华南,直播带货,支付宝,3.2,4
+ORD202400078,2024-03-07,服装鞋帽,运动鞋,300.21,3,900.63,0.0,华东,直播带货,支付宝,5.0,2
+ORD202400079,2024-02-02,服装鞋帽,T恤,79.31,1,79.31,0.0,东北,线上商城,微信支付,3.2,5
+ORD202400080,2024-09-02,食品饮料,橄榄油,103.47,1,93.12,0.1,华东,线上商城,微信支付,4.7,3
+ORD202400081,2024-11-01,服装鞋帽,牛仔裤,180.3,1,180.3,0.0,西南,直播带货,微信支付,3.6,4
+ORD202400082,2024-08-20,服装鞋帽,羽绒服,490.76,1,466.22,0.05,华北,线上商城,货到付款,4.5,4
+ORD202400083,2024-03-07,服装鞋帽,帽子,62.53,2,125.06,0.0,华北,线下门店,货到付款,5.0,1
+ORD202400084,2024-02-23,服装鞋帽,运动鞋,304.78,1,304.78,0.0,华北,线上商城,支付宝,5.0,2
+ORD202400085,2024-11-14,服装鞋帽,帽子,47.18,4,169.85,0.1,华东,线上商城,微信支付,4.1,3
+ORD202400086,2024-02-05,电子产品,智能手机,2808.09,1,2527.28,0.1,华北,线上商城,支付宝,4.1,2
+ORD202400087,2024-03-21,美妆个护,面膜礼盒,136.78,1,123.1,0.1,华北,线上商城,支付宝,4.3,3
+ORD202400088,2024-09-06,服装鞋帽,T恤,68.23,1,68.23,,华东,线上商城,支付宝,5.0,2
+ORD202400089,2024-03-30,电子产品,笔记本电脑,6154.39,1,5846.67,0.05,华东,线下门店,支付宝,4.2,4
+ORD202400090,2024-12-29,美妆个护,香水,371.51,1,334.36,0.1,华东,线下门店,微信支付,4.4,2
+ORD202400091,2024-07-24,服装鞋帽,牛仔裤,200.01,2,400.02,0.0,华东,线上商城,微信支付,4.9,3
+ORD202400092,2024-11-26,食品饮料,咖啡豆,66.06,1,62.76,0.05,华南,直播带货,微信支付,4.7,4
+ORD202400093,2024-08-17,美妆个护,防晒霜,99.08,5,470.63,0.05,华中,直播带货,支付宝,4.3,4
+ORD202400094,2024-09-16,电子产品,充电宝,118.27,1,112.36,0.05,华东,线下门店,银行卡,4.6,1
+ORD202400095,2024-01-22,服装鞋帽,帽子,57.51,1,51.76,0.1,华北,线下门店,微信支付,3.7,3
+ORD202400096,2024-01-21,家居用品,记忆枕,172.09,1,172.09,0.0,华北,线下门店,微信支付,3.8,3
+ORD202400097,2024-06-17,电子产品,充电宝,112.23,2,179.57,0.2,华中,线上商城,微信支付,4.9,3
+ORD202400098,2024-10-31,美妆个护,面膜礼盒,122.57,1,116.44,0.05,西南,直播带货,银行卡,3.3,4
+ORD202400099,2024-07-04,电子产品,智能手表,994.49,1,895.04,0.1,华东,线下门店,支付宝,5.0,2
+ORD202400100,2024-09-26,电子产品,智能手表,1224.21,2,2203.58,0.1,华东,线上商城,微信支付,4.2,1
+ORD202400101,2024-05-01,食品饮料,进口零食包,61.74,2,111.13,0.1,华东,线上商城,货到付款,3.9,2
+ORD202400102,2024-08-13,服装鞋帽,T恤,85.59,1,77.03,0.1,东北,直播带货,微信支付,3.9,5
+ORD202400103,2024-02-22,美妆个护,防晒霜,105.01,1,105.01,0.0,华东,线下门店,支付宝,5.0,1
+ORD202400104,2024-01-14,服装鞋帽,牛仔裤,180.11,1,180.11,0.0,华北,线上商城,货到付款,3.9,3
+ORD202400105,2024-08-29,美妆个护,洗发水,64.66,1,64.66,0.0,华中,线上商城,支付宝,5.0,4
+ORD202400106,2024-05-01,家居用品,空气炸锅,350.18,2,630.32,0.1,华东,线下门店,微信支付,4.8,1
+ORD202400107,2024-01-01,服装鞋帽,运动鞋,347.21,1,347.21,0.0,华北,线上商城,货到付款,4.8,1
+ORD202400108,2024-01-23,美妆个护,香水,388.96,1,388.96,0.0,华北,直播带货,微信支付,3.7,3
+ORD202400109,2024-11-25,家居用品,记忆枕,171.64,1,163.06,0.05,西南,线上商城,微信支付,4.5,3
+ORD202400110,2024-06-07,服装鞋帽,帽子,54.56,1,51.83,0.05,华东,线下门店,支付宝,4.7,2
+ORD202400111,2024-08-13,食品饮料,橄榄油,93.27,1,83.94,0.1,华北,直播带货,支付宝,4.4,4
+ORD202400112,2024-05-30,家居用品,空气炸锅,360.38,1,324.34,0.1,华南,线上商城,微信支付,4.7,3
+ORD202400113,2024-05-20,美妆个护,面膜礼盒,113.43,2,215.52,0.05,华北,直播带货,支付宝,3.8,5
+ORD202400114,2024-04-30,美妆个护,香水,402.63,4,1610.52,0.0,华南,直播带货,货到付款,5.0,3
+ORD202400115,2024-11-28,家居用品,空气炸锅,325.95,1,260.76,0.2,华北,线上商城,支付宝,5.0,2
+ORD202400116,2024-09-08,家居用品,吸尘器,829.39,1,746.45,0.1,西北,直播带货,货到付款,3.1,6
+ORD202400117,2024-06-02,食品饮料,咖啡豆,81.61,1,81.61,0.0,西北,直播带货,支付宝,4.1,4
+ORD202400118,2024-03-07,服装鞋帽,羽绒服,452.26,1,407.03,0.1,华北,直播带货,银行卡,4.2,5
+ORD202400119,2024-06-25,电子产品,笔记本电脑,6043.24,1,5741.08,0.05,华北,线下门店,支付宝,4.8,3
+ORD202400120,2024-06-26,食品饮料,进口零食包,73.82,3,199.31,0.1,华东,线下门店,支付宝,4.5,1
+ORD202400121,2024-10-24,电子产品,笔记本电脑,6183.9,2,12367.8,0.0,西南,线上商城,支付宝,4.0,3
+ORD202400122,2024-11-23,电子产品,笔记本电脑,5434.03,4,18475.7,0.15,华东,直播带货,微信支付,5.0,1
+ORD202400123,2024-06-23,服装鞋帽,T恤,87.57,1,78.81,0.1,东北,线上商城,支付宝,4.4,3
+ORD202400124,2024-02-19,电子产品,充电宝,133.11,1,133.11,0.0,西南,线下门店,微信支付,4.4,4
+ORD202400125,2024-09-11,电子产品,充电宝,120.2,5,601.0,0.0,华南,线上商城,支付宝,4.4,2
+ORD202400126,2024-02-25,服装鞋帽,T恤,84.39,2,160.34,0.05,华东,线上商城,微信支付,5.0,2
+ORD202400127,2024-11-15,美妆个护,面膜礼盒,115.52,1,103.97,0.1,西南,线上商城,货到付款,3.6,4
+ORD202400128,2024-03-11,美妆个护,电动牙刷,231.33,1,231.33,0.0,华东,直播带货,支付宝,4.1,4
+ORD202400129,2024-09-29,服装鞋帽,帽子,51.7,3,147.35,0.05,华东,线上商城,微信支付,5.0,1
+ORD202400130,2024-05-15,食品饮料,进口零食包,67.54,2,121.57,0.1,华东,直播带货,微信支付,4.3,3
+ORD202400131,2024-02-26,家居用品,记忆枕,141.62,1,141.62,0.0,华北,直播带货,货到付款,3.5,4
+ORD202400132,2024-06-15,食品饮料,橄榄油,98.75,1,93.81,0.05,华北,线上商城,微信支付,4.1,2
+ORD202400133,2024-09-04,家居用品,吸尘器,700.28,1,665.27,0.05,西南,直播带货,微信支付,4.6,3
+ORD202400134,2024-05-27,电子产品,笔记本电脑,5590.67,5,26555.68,0.05,东北,直播带货,支付宝,4.9,4
+ORD202400135,2024-09-02,家居用品,台灯,83.82,2,159.26,0.05,华东,直播带货,微信支付,5.0,3
+ORD202400136,2024-07-30,家居用品,记忆枕,132.02,3,396.06,0.0,东北,线下门店,支付宝,3.7,3
+ORD202400137,2024-05-27,家居用品,空气炸锅,426.5,1,426.5,0.0,华北,直播带货,货到付款,3.9,4
+ORD202400138,2024-07-22,美妆个护,洗发水,67.86,5,322.33,0.05,华中,线上商城,微信支付,4.5,5
+ORD202400139,2024-09-09,食品饮料,咖啡豆,82.9,1,82.9,0.0,西南,线上商城,微信支付,,3
+ORD202400140,2024-12-11,美妆个护,洗发水,62.0,2,105.4,0.15,西南,线上商城,支付宝,4.1,3
+ORD202400141,2024-05-13,服装鞋帽,牛仔裤,193.7,4,736.06,0.05,华南,线上商城,支付宝,4.8,3
+ORD202400142,2024-12-17,服装鞋帽,T恤,83.51,2,150.32,0.1,华北,线上商城,货到付款,4.9,2
+ORD202400143,2024-08-29,美妆个护,洗发水,64.71,2,129.42,0.0,华北,线上商城,支付宝,4.4,3
+ORD202400144,2024-10-01,电子产品,智能手表,1216.98,1,1216.98,0.0,华南,线上商城,银行卡,4.6,3
+ORD202400145,2024-10-13,服装鞋帽,T恤,84.29,1,84.29,0.0,西南,线上商城,支付宝,4.7,3
+ORD202400146,2024-08-29,电子产品,充电宝,108.48,1,97.63,0.1,华北,线下门店,微信支付,4.3,3
+ORD202400147,2024-03-09,美妆个护,香水,371.95,5,1673.78,0.1,华北,直播带货,支付宝,4.6,3
+ORD202400148,2024-09-29,家居用品,吸尘器,693.19,1,658.53,0.05,华东,线下门店,微信支付,4.7,3
+ORD202400149,2024-05-03,家居用品,空气炸锅,390.71,1,390.71,0.0,华东,线上商城,微信支付,4.8,1
+ORD202400150,2024-04-29,食品饮料,进口零食包,62.41,2,118.58,0.05,华东,线下门店,货到付款,4.9,1
+ORD202400151,2024-03-30,美妆个护,电动牙刷,233.85,1,222.16,0.05,东北,直播带货,微信支付,4.6,4
+ORD202400152,2024-08-30,家居用品,吸尘器,705.17,2,1269.31,0.1,华中,线上商城,货到付款,4.5,3
+ORD202400153,2024-08-18,食品饮料,坚果礼盒,88.94,3,266.82,0.0,华东,直播带货,微信支付,4.3,3
+ORD202400154,2024-09-02,家居用品,保温杯,61.16,4,244.64,0.0,华中,线下门店,支付宝,4.1,4
+ORD202400155,2024-05-02,服装鞋帽,T恤,76.17,1,72.36,0.05,华东,线上商城,银行卡,5.0,1
+ORD202400156,2024-04-14,家居用品,记忆枕,129.56,3,388.68,0.0,华东,线上商城,微信支付,4.6,2
+ORD202400157,2024-05-05,服装鞋帽,T恤,73.25,3,197.78,0.1,华北,直播带货,支付宝,4.6,4
+ORD202400158,2024-01-31,服装鞋帽,羽绒服,589.45,2,1178.9,0.0,华北,线下门店,支付宝,4.9,2
+ORD202400159,2024-12-07,食品饮料,橄榄油,102.68,1,82.14,0.2,华北,线上商城,微信支付,5.0,2
+ORD202400160,2024-04-01,电子产品,智能手机,3223.41,1,3062.24,0.05,西南,线上商城,微信支付,4.9,3
+ORD202400161,2024-12-16,食品饮料,坚果礼盒,92.06,3,276.18,0.0,华东,线上商城,微信支付,4.8,2
+ORD202400162,2024-06-18,服装鞋帽,羽绒服,581.59,4,2210.04,0.05,华东,线下门店,银行卡,5.0,2
+ORD202400163,2024-10-15,美妆个护,洗发水,67.72,1,67.72,0.0,华南,直播带货,微信支付,4.7,3
+ORD202400164,2024-07-18,美妆个护,防晒霜,90.2,2,180.4,0.0,西南,直播带货,支付宝,3.6,4
+ORD202400165,2024-11-05,美妆个护,防晒霜,92.06,3,276.18,0.0,华南,直播带货,微信支付,4.5,4
+ORD202400166,2024-07-13,电子产品,笔记本电脑,5443.96,50,272198.0,0.0,华东,线上商城,微信支付,4.4,1
+ORD202400167,2024-06-06,服装鞋帽,T恤,69.57,1,59.13,0.15,华北,线上商城,微信支付,4.6,3
+ORD202400168,2024-07-16,电子产品,充电宝,115.96,1,115.96,0.0,华南,线上商城,微信支付,4.9,3
+ORD202400169,2024-08-03,家居用品,空气炸锅,364.17,4,1311.01,0.1,华南,线上商城,支付宝,5.0,2
+ORD202400170,2024-06-17,服装鞋帽,牛仔裤,197.04,3,472.9,0.2,华东,线上商城,货到付款,5.0,2
+ORD202400171,2024-12-21,美妆个护,防晒霜,103.04,1,92.74,0.1,西南,线上商城,支付宝,4.1,3
+ORD202400172,2024-08-24,食品饮料,咖啡豆,73.62,1,73.62,0.0,西南,直播带货,微信支付,4.6,3
+ORD202400173,2024-07-12,电子产品,笔记本电脑,5763.87,1,5187.48,0.1,华北,直播带货,支付宝,3.7,4
+ORD202400174,2024-12-02,美妆个护,洗发水,62.65,1,53.25,0.15,东北,线上商城,微信支付,4.0,4
+ORD202400175,2024-12-18,服装鞋帽,牛仔裤,173.2,1,138.56,0.2,华南,线上商城,货到付款,4.8,2
+ORD202400176,2024-03-13,家居用品,保温杯,64.13,1,57.72,0.1,华北,线上商城,微信支付,5.0,2
+ORD202400177,2024-06-24,美妆个护,洗发水,67.74,2,108.38,0.2,华东,线上商城,微信支付,4.5,3
+ORD202400178,2024-08-09,服装鞋帽,帽子,61.32,1,55.19,0.1,华北,直播带货,支付宝,3.6,3
+ORD202400179,2024-04-05,家居用品,吸尘器,740.45,1,666.41,0.1,华北,直播带货,微信支付,4.6,2
+ORD202400180,2024-12-10,美妆个护,香水,472.75,2,898.22,0.05,华东,线上商城,支付宝,5.0,2
+ORD202400181,2024-08-04,家居用品,台灯,95.22,2,190.44,0.0,华北,线上商城,支付宝,5.0,2
+ORD202400182,2024-03-10,家居用品,记忆枕,170.87,1,153.78,0.1,华北,线上商城,微信支付,5.0,2
+ORD202400183,2024-01-17,家居用品,台灯,84.29,3,227.58,0.1,华北,线上商城,支付宝,3.6,3
+ORD202400184,2024-08-13,服装鞋帽,牛仔裤,158.53,3,475.59,0.0,华东,线下门店,微信支付,5.0,1
+ORD202400185,2024-05-01,家居用品,台灯,95.11,3,256.8,0.1,华东,线上商城,支付宝,5.0,2
+ORD202400186,2024-06-14,食品饮料,红茶礼盒,103.05,1,82.44,0.2,西北,直播带货,支付宝,3.0,6
+ORD202400187,2024-03-05,美妆个护,洗发水,55.49,2,110.98,0.0,西南,直播带货,微信支付,3.9,4
+ORD202400188,2024-02-17,服装鞋帽,T恤,83.61,3,225.75,0.1,东北,线上商城,支付宝,3.3,4
+ORD202400189,2024-07-02,食品饮料,进口零食包,63.97,5,319.85,0.0,华北,线上商城,支付宝,4.9,2
+ORD202400190,2024-01-25,电子产品,充电宝,102.59,4,410.36,0.0,华北,线上商城,货到付款,5.0,3
+ORD202400191,2024-05-20,电子产品,智能手表,1017.63,1,1017.63,0.0,华东,直播带货,微信支付,4.0,3
+ORD202400192,2024-02-09,电子产品,蓝牙耳机,261.52,1,261.52,0.0,华南,直播带货,货到付款,4.7,3
+ORD202400193,2024-02-22,电子产品,智能手表,1225.73,1,1225.73,0.0,华南,直播带货,支付宝,4.3,3
+ORD202400194,2024-03-18,食品饮料,进口零食包,69.69,3,209.07,0.0,华中,线上商城,支付宝,4.1,3
+ORD202400195,2024-11-26,电子产品,笔记本电脑,5975.74,2,10756.33,0.1,西南,直播带货,银行卡,3.5,4
+ORD202400196,2024-05-31,美妆个护,洗发水,59.91,1,53.92,0.1,华南,线上商城,银行卡,4.1,4
+ORD202400197,2024-08-03,家居用品,吸尘器,809.11,4,3236.44,0.0,华东,线上商城,支付宝,5.0,2
+ORD202400198,2024-09-13,美妆个护,电动牙刷,243.92,1,243.92,0.0,华东,线上商城,微信支付,5.0,2
+ORD202400199,2024-11-28,美妆个护,面膜礼盒,122.6,1,116.47,0.05,华东,线上商城,微信支付,5.0,1
+ORD202400200,2024-03-02,电子产品,蓝牙耳机,275.72,1,261.93,0.05,华北,直播带货,微信支付,4.8,3
+ORD202400201,2024-01-16,家居用品,记忆枕,139.74,1,139.74,0.0,华北,线上商城,支付宝,4.3,3
+ORD202400202,2024-12-11,美妆个护,洗发水,64.33,1,64.33,0.0,华东,线上商城,微信支付,5.0,3
+ORD202400203,2024-12-01,服装鞋帽,T恤,73.03,4,262.91,0.1,华东,线上商城,支付宝,4.8,1
+ORD202400204,2024-07-24,电子产品,充电宝,133.18,3,379.56,0.05,华东,线上商城,支付宝,5.0,2
+ORD202400205,2024-10-29,食品饮料,咖啡豆,80.23,1,80.23,,华东,线下门店,支付宝,4.8,1
+ORD202400206,2024-12-02,家居用品,保温杯,57.28,1,54.42,0.05,华北,线下门店,货到付款,4.7,3
+ORD202400207,2024-07-30,美妆个护,香水,414.77,2,829.54,0.0,华北,直播带货,微信支付,4.5,3
+ORD202400208,2024-06-10,食品饮料,橄榄油,103.9,2,187.02,0.1,华北,线上商城,货到付款,5.0,3
+ORD202400209,2024-06-09,服装鞋帽,帽子,52.66,3,134.28,0.15,华东,线上商城,支付宝,5.0,3
+ORD202400210,2024-06-16,电子产品,蓝牙耳机,296.54,1,296.54,0.0,华南,线上商城,货到付款,4.2,4
+ORD202400211,2024-08-24,电子产品,蓝牙耳机,230.47,1,230.47,0.0,华东,线下门店,支付宝,4.5,1
+ORD202400212,2024-01-12,食品饮料,咖啡豆,84.06,1,84.06,0.0,华东,线下门店,微信支付,5.0,3
+ORD202400213,2024-07-21,服装鞋帽,牛仔裤,189.89,1,189.89,0.0,华中,线上商城,微信支付,4.7,4
+ORD202400214,2024-10-08,服装鞋帽,羽绒服,485.65,1,485.65,0.0,华南,直播带货,支付宝,3.6,5
+ORD202400215,2024-09-02,服装鞋帽,牛仔裤,171.58,2,326.0,0.05,华北,线上商城,支付宝,5.0,3
+ORD202400216,2024-09-28,家居用品,台灯,103.49,5,517.45,0.0,华东,线上商城,微信支付,4.2,2
+ORD202400217,2024-07-17,家居用品,吸尘器,677.12,2,1286.53,0.05,华中,线上商城,微信支付,3.9,4
+ORD202400218,2024-09-09,服装鞋帽,运动鞋,339.02,1,339.02,0.0,华南,线上商城,微信支付,5.0,2
+ORD202400219,2024-06-14,电子产品,蓝牙耳机,266.76,4,1067.04,0.0,西南,线下门店,货到付款,5.0,2
+ORD202400220,2024-03-10,食品饮料,橄榄油,106.58,5,506.25,0.05,华东,线下门店,微信支付,4.2,3
+ORD202400221,2024-06-06,美妆个护,香水,373.05,2,596.88,0.2,华北,线下门店,货到付款,4.2,4
+ORD202400222,2024-10-16,电子产品,充电宝,107.06,2,214.12,0.0,华北,线上商城,微信支付,3.9,4
+ORD202400223,2024-01-06,家居用品,吸尘器,793.46,1,793.46,0.0,华南,线上商城,微信支付,4.7,3
+ORD202400224,2024-07-11,电子产品,充电宝,130.39,2,260.78,0.0,华东,线上商城,银行卡,5.0,2
+ORD202400225,2024-07-11,家居用品,空气炸锅,385.36,4,1541.44,0.0,华东,线下门店,银行卡,4.5,2
+ORD202400226,2024-01-28,电子产品,蓝牙耳机,297.0,50,14107.5,0.05,华南,线上商城,支付宝,5.0,2
+ORD202400227,2024-02-14,家居用品,台灯,89.47,1,89.47,0.0,华东,线上商城,支付宝,4.6,2
+ORD202400228,2024-02-16,美妆个护,电动牙刷,208.26,3,593.54,0.05,华东,线上商城,微信支付,5.0,2
+ORD202400229,2024-02-19,电子产品,智能手表,1201.7,1,1201.7,0.0,华北,线下门店,微信支付,5.0,2
+ORD202400230,2024-08-31,家居用品,记忆枕,168.12,2,336.24,0.0,华东,直播带货,支付宝,3.5,5
+ORD202400231,2024-12-15,服装鞋帽,运动鞋,374.08,1,374.08,,华东,线上商城,微信支付,3.9,3
+ORD202400232,2024-10-27,服装鞋帽,牛仔裤,159.08,1,159.08,0.0,华中,线上商城,支付宝,5.0,3
+ORD202400233,2024-09-05,服装鞋帽,牛仔裤,155.02,1,155.02,0.0,华南,直播带货,微信支付,5.0,2
+ORD202400234,2024-05-17,食品饮料,坚果礼盒,83.72,3,238.6,0.05,华东,线上商城,微信支付,5.0,3
+ORD202400235,2024-06-13,家居用品,记忆枕,154.72,1,131.51,0.15,华东,线下门店,微信支付,4.0,2
+ORD202400236,2024-06-08,美妆个护,电动牙刷,204.86,3,491.66,0.2,华东,线上商城,银行卡,4.7,2
+ORD202400237,2024-05-20,家居用品,记忆枕,150.23,2,300.46,0.0,华南,线上商城,微信支付,5.0,1
+ORD202400238,2024-05-12,美妆个护,面膜礼盒,121.62,1,121.62,0.0,西南,直播带货,支付宝,4.5,4
+ORD202400239,2024-12-15,电子产品,蓝牙耳机,271.49,2,542.98,0.0,华东,直播带货,货到付款,4.0,2
+ORD202400240,2024-08-23,家居用品,记忆枕,141.1,1,126.99,0.1,华南,线上商城,货到付款,3.9,4
+ORD202400241,2024-12-16,电子产品,智能手机,3042.83,1,2738.55,0.1,华中,线上商城,微信支付,3.6,4
+ORD202400242,2024-11-03,服装鞋帽,T恤,83.28,2,141.58,0.15,西北,线上商城,微信支付,4.0,4
+ORD202400243,2024-04-23,美妆个护,电动牙刷,258.05,2,490.3,0.05,西南,线下门店,银行卡,4.3,4
+ORD202400244,2024-04-03,食品饮料,红茶礼盒,115.1,1,115.1,0.0,西南,线上商城,微信支付,4.9,3
+ORD202400245,2024-11-20,服装鞋帽,帽子,47.93,3,115.03,0.2,华东,直播带货,微信支付,5.0,1
+ORD202400246,2024-10-18,食品饮料,橄榄油,87.51,3,262.53,0.0,西南,线上商城,微信支付,,4
+ORD202400247,2024-03-27,服装鞋帽,运动鞋,338.94,2,677.88,0.0,华东,线上商城,微信支付,,1
+ORD202400248,2024-01-08,食品饮料,进口零食包,59.87,2,119.74,0.0,华东,线下门店,支付宝,4.9,3
+ORD202400249,2024-05-28,服装鞋帽,帽子,56.59,1,56.59,0.0,华北,线下门店,支付宝,5.0,3
+ORD202400250,2024-03-08,家居用品,空气炸锅,390.22,1,370.71,0.05,华南,直播带货,支付宝,3.9,3
+ORD202400251,2024-02-25,美妆个护,面膜礼盒,112.38,1,101.14,0.1,华北,线上商城,微信支付,5.0,3
+ORD202400252,2024-12-05,服装鞋帽,帽子,56.74,1,56.74,0.0,华北,直播带货,支付宝,3.8,3
+ORD202400253,2024-04-13,家居用品,记忆枕,140.79,1,126.71,0.1,华中,直播带货,微信支付,4.2,4
+ORD202400254,2024-09-09,电子产品,智能手机,3468.35,1,3468.35,0.0,华中,直播带货,微信支付,4.0,4
+ORD202400255,2024-05-28,家居用品,保温杯,63.56,1,63.56,0.0,华东,线上商城,微信支付,5.0,1
+ORD202400256,2024-07-09,食品饮料,咖啡豆,66.31,1,59.68,0.1,华东,线上商城,银行卡,4.3,2
+ORD202400257,2024-01-31,食品饮料,咖啡豆,66.08,4,237.89,0.1,西南,直播带货,支付宝,4.4,4
+ORD202400258,2024-03-07,服装鞋帽,牛仔裤,159.1,1,159.1,0.0,华东,直播带货,支付宝,4.1,3
+ORD202400259,2024-12-08,食品饮料,进口零食包,66.46,2,132.92,0.0,华北,直播带货,微信支付,3.7,3
+ORD202400260,2024-11-09,电子产品,蓝牙耳机,260.43,1,221.37,0.15,华中,线上商城,微信支付,5.0,3
+ORD202400261,2024-07-25,食品饮料,坚果礼盒,86.92,3,234.68,0.1,华东,线上商城,支付宝,4.4,3
+ORD202400262,2024-10-08,电子产品,智能手机,3480.11,5,16530.52,0.05,华中,线上商城,微信支付,5.0,2
+ORD202400263,2024-06-18,服装鞋帽,帽子,56.65,2,101.97,0.1,华南,线上商城,微信支付,5.0,3
+ORD202400264,2024-09-24,家居用品,空气炸锅,328.22,1,328.22,0.0,西南,直播带货,微信支付,4.4,4
+ORD202400265,2024-04-07,美妆个护,面膜礼盒,140.03,1,140.03,0.0,西南,线上商城,微信支付,4.0,4
+ORD202400266,2024-08-09,食品饮料,橄榄油,110.48,3,331.44,0.0,华南,线上商城,微信支付,4.5,4
+ORD202400267,2024-08-02,食品饮料,咖啡豆,69.91,5,332.07,0.05,华北,线上商城,微信支付,4.2,2
+ORD202400268,2024-12-30,美妆个护,防晒霜,98.78,1,79.02,0.2,华南,直播带货,货到付款,4.5,4
+ORD202400269,2024-11-22,家居用品,空气炸锅,409.73,2,655.57,0.2,华南,直播带货,微信支付,2.9,4
+ORD202400270,2024-09-24,电子产品,充电宝,107.23,2,214.46,0.0,华东,直播带货,货到付款,4.7,2
+ORD202400271,2024-10-04,电子产品,充电宝,105.69,3,317.07,0.0,西南,线上商城,支付宝,3.2,4
+ORD202400272,2024-07-17,服装鞋帽,T恤,90.04,1,85.54,0.05,华东,线下门店,银行卡,5.0,1
+ORD202400273,2024-10-28,服装鞋帽,帽子,49.99,2,99.98,0.0,华东,线上商城,微信支付,5.0,2
+ORD202400274,2024-10-11,家居用品,吸尘器,768.9,1,730.45,0.05,华北,线下门店,支付宝,4.3,3
+ORD202400275,2024-06-03,服装鞋帽,帽子,55.83,1,50.25,0.1,华北,线下门店,微信支付,4.3,3
+ORD202400276,2024-04-19,美妆个护,香水,402.46,1,362.21,0.1,西北,线下门店,货到付款,3.2,6
+ORD202400277,2024-11-14,家居用品,空气炸锅,400.81,1,400.81,0.0,华北,线上商城,支付宝,3.4,4
+ORD202400278,2024-01-23,家居用品,吸尘器,709.57,1,709.57,0.0,华东,线下门店,微信支付,5.0,2
+ORD202400279,2024-08-05,食品饮料,咖啡豆,75.85,2,136.53,0.1,华南,线下门店,微信支付,4.6,4
+ORD202400280,2024-05-08,服装鞋帽,帽子,60.31,4,217.12,0.1,华北,直播带货,微信支付,4.2,4
+ORD202400281,2024-10-26,电子产品,智能手机,3063.39,1,2910.22,0.05,西南,线上商城,支付宝,3.6,4
+ORD202400282,2024-09-30,家居用品,记忆枕,163.35,3,465.55,0.05,东北,线上商城,微信支付,3.2,5
+ORD202400283,2024-10-10,食品饮料,咖啡豆,66.52,1,59.87,0.1,华北,线下门店,支付宝,3.9,2
+ORD202400284,2024-09-19,电子产品,蓝牙耳机,238.22,3,714.66,0.0,华东,直播带货,微信支付,4.4,4
+ORD202400285,2024-02-07,家居用品,空气炸锅,351.58,1,316.42,0.1,东北,线上商城,微信支付,4.5,4
+ORD202400286,2024-06-17,服装鞋帽,T恤,83.58,1,66.86,0.2,华北,线上商城,微信支付,4.6,3
+ORD202400287,2024-07-03,电子产品,智能手表,1224.7,1,1163.46,0.05,西南,线上商城,支付宝,4.4,3
+ORD202400288,2024-04-10,家居用品,空气炸锅,405.93,4,1623.72,0.0,西南,线上商城,微信支付,4.2,4
+ORD202400289,2024-11-30,美妆个护,香水,406.45,4,1300.64,0.2,华南,线上商城,微信支付,4.8,2
+ORD202400290,2024-11-10,美妆个护,防晒霜,84.75,1,67.8,0.2,东北,直播带货,货到付款,2.9,5
+ORD202400291,2024-02-02,家居用品,吸尘器,647.13,1,614.77,0.05,华北,线下门店,银行卡,4.3,4
+ORD202400292,2024-05-20,食品饮料,红茶礼盒,112.59,2,202.66,0.1,华南,线上商城,微信支付,4.0,3
+ORD202400293,2024-06-29,食品饮料,红茶礼盒,109.57,1,87.66,0.2,华东,线下门店,微信支付,5.0,2
+ORD202400294,2024-11-04,美妆个护,面膜礼盒,136.28,1,122.65,0.1,华南,线上商城,微信支付,4.6,3
+ORD202400295,2024-10-20,服装鞋帽,运动鞋,353.21,1,353.21,0.0,华东,线上商城,支付宝,4.4,3
+ORD202400296,2024-12-04,食品饮料,坚果礼盒,80.52,2,136.88,0.15,华中,线上商城,银行卡,4.3,2
+ORD202400297,2024-05-15,家居用品,保温杯,73.24,2,146.48,0.0,华东,线上商城,支付宝,5.0,2
+ORD202400298,2024-02-19,家居用品,台灯,92.69,3,278.07,0.0,华东,直播带货,银行卡,4.5,3
+ORD202400299,2024-08-03,家居用品,空气炸锅,402.53,1,362.28,0.1,西南,线上商城,微信支付,3.7,3
+ORD202400300,2024-09-01,美妆个护,面膜礼盒,121.55,3,328.19,0.1,华东,线上商城,微信支付,4.6,3
+ORD202400301,2024-06-10,服装鞋帽,羽绒服,466.15,2,839.07,0.1,华东,线上商城,微信支付,4.0,1
+ORD202400302,2024-10-12,服装鞋帽,T恤,80.4,1,76.38,0.05,华南,直播带货,微信支付,4.4,5
+ORD202400303,2024-03-23,电子产品,笔记本电脑,5626.98,1,5626.98,0.0,华东,线下门店,支付宝,5.0,2
+ORD202400304,2024-04-23,家居用品,台灯,77.52,1,69.77,0.1,华南,线上商城,微信支付,4.3,1
+ORD202400305,2024-04-13,食品饮料,进口零食包,61.76,1,61.76,0.0,华南,直播带货,支付宝,3.9,2
+ORD202400306,2024-12-08,服装鞋帽,帽子,50.06,1,50.06,0.0,华中,线上商城,微信支付,4.6,2
+ORD202400307,2024-04-09,服装鞋帽,牛仔裤,184.04,1,165.64,0.1,华北,线上商城,微信支付,5.0,2
+ORD202400308,2024-10-21,家居用品,记忆枕,170.97,2,341.94,0.0,西南,线下门店,微信支付,5.0,2
+ORD202400309,2024-11-20,美妆个护,洗发水,63.18,1,56.86,0.1,华北,线上商城,微信支付,4.4,2
+ORD202400310,2024-06-24,电子产品,蓝牙耳机,258.17,1,206.54,0.2,华东,线下门店,微信支付,4.2,3
+ORD202400311,2024-04-15,家居用品,吸尘器,825.68,1,825.68,0.0,华东,线上商城,货到付款,5.0,2
+ORD202400312,2024-07-09,美妆个护,电动牙刷,257.47,3,733.79,0.05,华北,线下门店,银行卡,5.0,2
+ORD202400313,2024-09-01,食品饮料,坚果礼盒,88.07,1,88.07,0.0,华北,线上商城,微信支付,5.0,2
+ORD202400314,2024-07-04,服装鞋帽,T恤,71.59,1,68.01,0.05,华北,直播带货,支付宝,4.6,3
+ORD202400315,2024-05-23,服装鞋帽,帽子,48.91,2,97.82,0.0,西南,直播带货,微信支付,4.2,3
+ORD202400316,2024-08-30,食品饮料,咖啡豆,73.23,1,69.57,0.05,西南,线上商城,微信支付,4.3,3
+ORD202400317,2024-08-31,食品饮料,坚果礼盒,86.68,1,82.35,0.05,华北,线上商城,微信支付,5.0,2
+ORD202400318,2024-10-22,服装鞋帽,帽子,49.63,3,141.45,0.05,华东,线上商城,微信支付,5.0,2
+ORD202400319,2024-03-26,美妆个护,香水,410.15,2,738.27,0.1,西南,线上商城,支付宝,4.5,4
+ORD202400320,2024-05-18,食品饮料,红茶礼盒,118.18,1,106.36,0.1,华北,线上商城,支付宝,4.3,2
+ORD202400321,2024-03-26,服装鞋帽,羽绒服,451.95,3,1355.85,0.0,华东,线上商城,微信支付,4.7,3
+ORD202400322,2024-06-07,家居用品,保温杯,63.89,1,51.11,0.2,西南,线上商城,微信支付,3.8,4
+ORD202400323,2024-05-19,服装鞋帽,运动鞋,387.89,2,775.78,0.0,华南,线上商城,微信支付,3.7,4
+ORD202400324,2024-09-03,服装鞋帽,羽绒服,510.93,1,485.38,0.05,华东,线上商城,微信支付,4.7,2
+ORD202400325,2024-09-08,食品饮料,橄榄油,92.11,2,175.01,0.05,华南,直播带货,微信支付,5.0,4
+ORD202400326,2024-11-26,服装鞋帽,牛仔裤,164.91,1,156.66,0.05,华北,直播带货,微信支付,4.0,3
+ORD202400327,2024-04-06,食品饮料,咖啡豆,66.82,1,60.14,0.1,华南,线上商城,微信支付,4.6,3
+ORD202400328,2024-02-22,家居用品,吸尘器,798.42,2,1596.84,0.0,华东,线上商城,支付宝,5.0,1
+ORD202400329,2024-01-17,家居用品,吸尘器,650.11,3,1950.33,0.0,华北,线上商城,银行卡,,2
+ORD202400330,2024-07-16,美妆个护,电动牙刷,212.55,5,1062.75,0.0,华北,直播带货,微信支付,4.0,4
+ORD202400331,2024-11-18,美妆个护,防晒霜,95.6,1,86.04,0.1,华东,线下门店,银行卡,5.0,2
+ORD202400332,2024-05-21,服装鞋帽,牛仔裤,195.61,1,195.61,0.0,华南,直播带货,微信支付,4.8,4
+ORD202400333,2024-06-22,食品饮料,坚果礼盒,76.38,2,122.21,0.2,西南,线上商城,支付宝,3.5,5
+ORD202400334,2024-04-08,电子产品,充电宝,114.89,1,114.89,0.0,华北,线上商城,支付宝,4.7,2
+ORD202400335,2024-05-20,食品饮料,进口零食包,74.21,2,148.42,0.0,华北,线上商城,微信支付,4.9,2
+ORD202400336,2024-01-10,服装鞋帽,T恤,85.54,1,85.54,0.0,华南,线上商城,微信支付,4.9,2
+ORD202400337,2024-11-24,家居用品,空气炸锅,382.64,1,363.51,0.05,华东,线上商城,支付宝,4.9,2
+ORD202400338,2024-11-15,美妆个护,香水,363.18,1,363.18,0.0,华东,线下门店,微信支付,3.9,3
+ORD202400339,2024-07-31,食品饮料,坚果礼盒,96.54,1,96.54,0.0,华南,线下门店,支付宝,5.0,3
+ORD202400340,2024-04-08,服装鞋帽,运动鞋,347.14,4,1249.7,0.1,西南,直播带货,微信支付,3.4,4
+ORD202400341,2024-10-08,服装鞋帽,T恤,82.73,1,74.46,0.1,华中,线下门店,微信支付,3.6,5
+ORD202400342,2024-06-14,服装鞋帽,帽子,55.75,1,44.6,0.2,华中,线上商城,货到付款,5.0,2
+ORD202400343,2024-09-22,电子产品,智能手表,937.17,5,4685.85,0.0,西南,线上商城,银行卡,4.2,3
+ORD202400344,2024-03-20,电子产品,充电宝,114.8,1,109.06,0.05,华中,线上商城,支付宝,5.0,3
+ORD202400345,2024-06-15,美妆个护,洗发水,59.79,4,191.33,0.2,西南,线上商城,微信支付,3.9,5
+ORD202400346,2024-06-20,家居用品,记忆枕,146.24,3,416.78,0.05,西南,线下门店,货到付款,4.0,3
+ORD202400347,2024-10-16,电子产品,笔记本电脑,6144.12,1,5529.71,0.1,东北,直播带货,微信支付,3.9,5
+ORD202400348,2024-08-31,服装鞋帽,羽绒服,527.51,1,474.76,0.1,华北,直播带货,支付宝,4.6,4
+ORD202400349,2024-11-11,食品饮料,进口零食包,60.98,1,51.83,0.15,华东,线下门店,支付宝,4.3,2
+ORD202400350,2024-01-13,美妆个护,洗发水,68.78,2,123.8,0.1,华南,线上商城,货到付款,5.0,3
+ORD202400351,2024-01-28,家居用品,台灯,101.52,3,289.33,0.05,华东,直播带货,支付宝,5.0,2
+ORD202400352,2024-09-24,美妆个护,防晒霜,94.52,2,189.04,0.0,华南,直播带货,支付宝,3.8,4
+ORD202400353,2024-12-22,服装鞋帽,羽绒服,514.53,4,2058.12,0.0,华北,线上商城,银行卡,4.0,2
+ORD202400354,2024-04-28,食品饮料,进口零食包,75.44,1,71.67,0.05,华北,直播带货,支付宝,5.0,4
+ORD202400355,2024-09-28,家居用品,记忆枕,145.2,1,137.94,0.05,华南,线上商城,支付宝,3.6,5
+ORD202400356,2024-07-17,食品饮料,坚果礼盒,99.73,4,398.92,0.0,华东,线下门店,微信支付,4.8,3
+ORD202400357,2024-04-26,服装鞋帽,帽子,49.27,5,234.03,0.05,华北,线上商城,微信支付,4.6,1
+ORD202400358,2024-09-25,食品饮料,橄榄油,112.57,5,562.85,0.0,华南,线上商城,支付宝,4.4,4
+ORD202400359,2024-03-15,美妆个护,香水,467.9,1,421.11,0.1,华东,直播带货,银行卡,4.4,2
+ORD202400360,2024-01-14,美妆个护,防晒霜,98.33,2,196.66,0.0,华南,线上商城,支付宝,4.5,4
+ORD202400361,2024-11-28,家居用品,记忆枕,149.62,1,142.14,0.05,华东,线上商城,货到付款,3.3,4
+ORD202400362,2024-04-14,电子产品,充电宝,133.22,1,126.56,0.05,华中,线下门店,微信支付,3.9,3
+ORD202400363,2024-10-19,服装鞋帽,帽子,57.33,2,114.66,0.0,华北,线上商城,银行卡,4.1,5
+ORD202400364,2024-10-12,服装鞋帽,羽绒服,471.62,1,448.04,0.05,西南,线上商城,支付宝,5.0,3
+ORD202400365,2024-11-26,服装鞋帽,帽子,48.62,3,116.69,0.2,华东,线上商城,微信支付,5.0,2
+ORD202400366,2024-05-08,家居用品,吸尘器,799.13,1,799.13,0.0,华南,直播带货,支付宝,3.8,3
+ORD202400367,2024-10-08,服装鞋帽,运动鞋,382.05,2,764.1,0.0,华东,线上商城,微信支付,4.7,3
+ORD202400368,2024-04-02,电子产品,蓝牙耳机,295.91,1,281.11,0.05,华东,线上商城,微信支付,4.4,2
+ORD202400369,2024-08-20,服装鞋帽,羽绒服,535.79,1,509.0,0.05,华南,线上商城,银行卡,4.6,3
+ORD202400370,2024-12-07,服装鞋帽,帽子,51.67,4,206.68,0.0,华东,线下门店,支付宝,5.0,2
+ORD202400371,2024-08-21,美妆个护,香水,474.37,1,450.65,0.05,华东,线下门店,银行卡,4.0,2
+ORD202400372,2024-05-20,家居用品,记忆枕,151.11,2,302.22,0.0,华东,直播带货,微信支付,4.1,4
+ORD202400373,2024-05-27,服装鞋帽,T恤,73.3,2,139.27,0.05,华中,直播带货,微信支付,4.0,3
+ORD202400374,2024-05-28,食品饮料,坚果礼盒,77.92,1,77.92,0.0,华南,线上商城,微信支付,4.6,3
+ORD202400375,2024-11-23,家居用品,保温杯,60.21,1,54.19,0.1,华北,线上商城,支付宝,4.4,2
+ORD202400376,2024-03-30,家居用品,保温杯,73.23,1,65.91,0.1,西南,直播带货,微信支付,3.8,3
+ORD202400377,2024-07-30,食品饮料,坚果礼盒,96.85,1,92.01,0.05,华北,线上商城,微信支付,5.0,2
+ORD202400378,2024-01-16,家居用品,记忆枕,155.69,3,420.36,0.1,华东,线上商城,微信支付,5.0,1
+ORD202400379,2024-05-01,家居用品,保温杯,59.46,1,59.46,0.0,华北,线上商城,支付宝,4.9,3
+ORD202400380,2024-06-23,食品饮料,进口零食包,62.25,1,62.25,0.0,西南,线下门店,微信支付,4.7,4
+ORD202400381,2024-01-03,家居用品,空气炸锅,401.32,2,802.64,0.0,华东,线下门店,支付宝,4.2,2
+ORD202400382,2024-04-17,家居用品,吸尘器,701.98,1,631.78,0.1,华南,线上商城,支付宝,4.5,5
+ORD202400383,2024-10-08,家居用品,吸尘器,849.74,3,2549.22,0.0,华东,线上商城,银行卡,5.0,2
+ORD202400384,2024-07-15,电子产品,充电宝,116.11,2,209.0,0.1,华南,线上商城,微信支付,3.8,3
+ORD202400385,2024-08-26,服装鞋帽,羽绒服,568.8,4,2047.68,0.1,华东,线上商城,微信支付,4.4,2
+ORD202400386,2024-05-12,食品饮料,橄榄油,88.69,4,337.02,0.05,西南,线下门店,微信支付,4.2,4
+ORD202400387,2024-06-27,服装鞋帽,运动鞋,340.81,1,340.81,0.0,华东,直播带货,微信支付,4.9,2
+ORD202400388,2024-09-03,服装鞋帽,T恤,72.09,2,129.76,0.1,西南,线上商城,微信支付,4.4,3
+ORD202400389,2024-10-20,食品饮料,坚果礼盒,90.43,1,90.43,0.0,西南,线上商城,货到付款,4.2,4
+ORD202400390,2024-09-10,美妆个护,面膜礼盒,114.03,3,342.09,0.0,西南,直播带货,微信支付,4.8,4
+ORD202400391,2024-06-02,服装鞋帽,T恤,88.92,4,320.11,0.1,西北,直播带货,微信支付,4.0,4
+ORD202400392,2024-01-28,电子产品,笔记本电脑,6358.11,1,6040.2,0.05,华中,线上商城,微信支付,5.0,2
+ORD202400393,2024-12-05,家居用品,吸尘器,679.85,1,611.87,0.1,西南,线上商城,货到付款,4.1,3
+ORD202400394,2024-09-06,美妆个护,洗发水,52.52,2,94.54,0.1,华中,线下门店,微信支付,3.8,4
+ORD202400395,2024-07-15,家居用品,吸尘器,733.07,1,659.76,0.1,西南,线上商城,微信支付,4.9,3
+ORD202400396,2024-12-26,家居用品,台灯,89.5,2,170.05,0.05,华南,线上商城,银行卡,3.9,3
+ORD202400397,2024-05-05,服装鞋帽,羽绒服,548.72,1,521.28,0.05,华南,线下门店,微信支付,4.1,4
+ORD202400398,2024-06-29,食品饮料,红茶礼盒,103.04,1,82.43,0.2,西南,直播带货,微信支付,3.9,5
+ORD202400399,2024-11-15,电子产品,充电宝,104.97,1,94.47,0.1,华东,直播带货,支付宝,5.0,3
+ORD202400400,2024-12-03,服装鞋帽,运动鞋,315.65,2,536.6,0.15,华东,线上商城,支付宝,3.8,4
+ORD202400401,2024-07-30,美妆个护,洗发水,62.89,2,125.78,0.0,华东,直播带货,支付宝,4.3,2
+ORD202400402,2024-06-03,服装鞋帽,帽子,50.61,2,86.04,0.15,华北,线上商城,银行卡,4.2,4
+ORD202400403,2024-12-15,食品饮料,坚果礼盒,99.03,2,168.35,0.15,华南,直播带货,支付宝,5.0,3
+ORD202400404,2024-12-11,美妆个护,防晒霜,102.71,1,97.57,0.05,华东,线上商城,支付宝,4.6,3
+ORD202400405,2024-05-25,家居用品,空气炸锅,325.7,1,325.7,0.0,东北,直播带货,微信支付,3.9,6
+ORD202400406,2024-10-16,食品饮料,咖啡豆,83.91,2,159.43,0.05,华东,直播带货,支付宝,5.0,1
+ORD202400407,2024-06-14,家居用品,空气炸锅,354.61,1,301.42,0.15,华东,线上商城,货到付款,5.0,2
+ORD202400408,2024-04-01,美妆个护,面膜礼盒,116.11,2,232.22,0.0,华南,线上商城,微信支付,4.7,3
+ORD202400409,2024-01-10,食品饮料,咖啡豆,78.44,1,78.44,0.0,华南,线上商城,微信支付,3.7,4
+ORD202400410,2024-08-11,服装鞋帽,羽绒服,526.26,1,473.63,0.1,华东,线上商城,货到付款,5.0,2
+ORD202400411,2024-07-06,服装鞋帽,T恤,69.06,1,65.61,0.05,华南,线上商城,支付宝,4.8,3
+ORD202400412,2024-07-02,服装鞋帽,牛仔裤,192.71,1,192.71,0.0,华东,线下门店,支付宝,5.0,1
+ORD202400413,2024-08-18,食品饮料,坚果礼盒,75.78,1,75.78,0.0,华北,线上商城,微信支付,4.8,1
+ORD202400414,2024-04-29,食品饮料,坚果礼盒,94.22,1,94.22,0.0,华北,线下门店,支付宝,4.7,2
+ORD202400415,2024-02-19,服装鞋帽,运动鞋,374.21,1,374.21,0.0,华东,直播带货,微信支付,4.7,3
+ORD202400416,2024-07-24,家居用品,台灯,91.24,1,91.24,0.0,西南,直播带货,支付宝,4.3,4
+ORD202400417,2024-08-12,家居用品,台灯,77.6,1,69.84,0.1,西南,线上商城,微信支付,4.1,4
+ORD202400418,2024-08-01,美妆个护,电动牙刷,243.27,1,231.11,0.05,华北,线上商城,货到付款,4.9,2
+ORD202400419,2024-11-17,食品饮料,红茶礼盒,104.98,1,99.73,0.05,华东,线下门店,货到付款,4.1,1
+ORD202400420,2024-11-16,美妆个护,香水,363.69,1,345.51,0.05,华南,线下门店,微信支付,4.6,4
+ORD202400421,2024-08-10,家居用品,台灯,98.24,2,196.48,0.0,华东,线上商城,微信支付,5.0,2
+ORD202400422,2024-08-15,服装鞋帽,T恤,77.08,2,146.45,0.05,华东,线上商城,支付宝,5.0,2
+ORD202400423,2024-10-26,服装鞋帽,牛仔裤,160.83,4,578.99,0.1,华东,线上商城,微信支付,5.0,2
+ORD202400424,2024-10-18,电子产品,蓝牙耳机,229.67,1,206.7,0.1,华东,线上商城,银行卡,4.5,1
+ORD202400425,2024-10-10,食品饮料,坚果礼盒,85.67,1,85.67,0.0,华南,线下门店,支付宝,3.7,3
+ORD202400426,2024-10-29,食品饮料,进口零食包,73.89,1,73.89,0.0,华东,线上商城,微信支付,4.7,3
+ORD202400427,2024-08-19,服装鞋帽,羽绒服,529.12,1,476.21,0.1,东北,线上商城,支付宝,3.9,4
+ORD202400428,2024-10-19,美妆个护,面膜礼盒,148.58,1,148.58,0.0,华中,线上商城,微信支付,5.0,2
+ORD202400429,2024-12-19,家居用品,空气炸锅,356.81,2,570.9,0.2,华北,直播带货,微信支付,4.0,5
+ORD202400430,2024-12-11,家居用品,空气炸锅,335.65,1,268.52,0.2,华东,线上商城,微信支付,4.2,2
+ORD202400431,2024-10-03,电子产品,充电宝,128.55,2,257.1,0.0,西南,直播带货,支付宝,3.3,5
+ORD202400432,2024-08-19,服装鞋帽,帽子,60.44,1,57.42,0.05,华北,线上商城,微信支付,3.7,2
+ORD202400433,2024-02-14,电子产品,蓝牙耳机,243.49,4,973.96,0.0,华东,线上商城,支付宝,5.0,2
+ORD202400434,2024-12-15,服装鞋帽,运动鞋,337.34,1,337.34,0.0,华东,线上商城,银行卡,5.0,2
+ORD202400435,2024-11-26,美妆个护,电动牙刷,236.44,2,378.3,0.2,华北,线上商城,支付宝,4.1,3
+ORD202400436,2024-01-25,电子产品,智能手表,1085.46,1,1085.46,0.0,华南,线上商城,货到付款,3.4,4
+ORD202400437,2024-01-22,食品饮料,进口零食包,58.6,1,58.6,0.0,华东,直播带货,微信支付,4.5,3
+ORD202400438,2024-03-03,食品饮料,橄榄油,110.67,2,199.21,0.1,华中,线上商城,货到付款,4.8,2
+ORD202400439,2024-10-20,美妆个护,防晒霜,101.51,4,385.74,0.05,华南,直播带货,银行卡,4.6,4
+ORD202400440,2024-08-16,家居用品,保温杯,56.32,2,112.64,0.0,西南,直播带货,支付宝,4.1,5
+ORD202400441,2024-07-20,家居用品,保温杯,67.01,1,67.01,0.0,华中,线上商城,支付宝,4.2,3
+ORD202400442,2024-08-16,食品饮料,坚果礼盒,80.49,3,217.32,0.1,西南,线上商城,微信支付,4.0,4
+ORD202400443,2024-02-14,电子产品,智能手表,1091.62,3,3111.12,0.05,华东,线下门店,微信支付,4.8,3
+ORD202400444,2024-12-29,服装鞋帽,运动鞋,309.32,3,835.16,0.1,华北,线上商城,银行卡,4.0,3
+ORD202400445,2024-01-02,电子产品,智能手表,1250.49,3,3376.32,0.1,华东,直播带货,支付宝,5.0,3
+ORD202400446,2024-09-09,食品饮料,坚果礼盒,90.61,1,81.55,0.1,华南,线下门店,微信支付,5.0,2
+ORD202400447,2024-05-12,家居用品,空气炸锅,339.66,1,339.66,0.0,华北,线上商城,支付宝,4.1,1
+ORD202400448,2024-02-09,服装鞋帽,运动鞋,367.0,2,734.0,0.0,华北,线下门店,银行卡,4.6,2
+ORD202400449,2024-10-23,家居用品,吸尘器,640.4,1,608.38,0.05,西南,线上商城,货到付款,4.4,4
+ORD202400450,2024-10-13,服装鞋帽,帽子,60.71,2,121.42,0.0,华北,线下门店,支付宝,4.6,3
+ORD202400451,2024-08-14,食品饮料,进口零食包,66.46,5,332.3,0.0,华南,线上商城,微信支付,5.0,3
+ORD202400452,2024-03-07,食品饮料,橄榄油,103.61,3,310.83,0.0,华东,线上商城,微信支付,5.0,2
+ORD202400453,2024-05-09,电子产品,智能手表,1155.3,2,2310.6,0.0,华中,线上商城,银行卡,5.0,3
+ORD202400454,2024-10-23,家居用品,吸尘器,829.91,3,2489.73,0.0,华北,线上商城,微信支付,3.9,3
+ORD202400455,2024-10-02,电子产品,智能手机,3607.26,4,12986.14,0.1,华北,直播带货,微信支付,4.0,4
+ORD202400456,2024-04-26,服装鞋帽,T恤,70.08,1,70.08,0.0,华北,线上商城,支付宝,4.8,2
+ORD202400457,2024-06-08,美妆个护,电动牙刷,211.09,1,179.43,0.15,华东,线上商城,银行卡,5.0,1
+ORD202400458,2024-02-23,服装鞋帽,帽子,57.62,1,57.62,0.0,华北,直播带货,货到付款,3.5,3
+ORD202400459,2024-04-12,服装鞋帽,牛仔裤,159.63,3,431.0,0.1,华东,直播带货,微信支付,5.0,2
+ORD202400460,2024-11-04,食品饮料,橄榄油,98.34,1,83.59,0.15,华东,线上商城,微信支付,4.6,2
+ORD202400461,2024-12-13,电子产品,蓝牙耳机,250.85,2,476.61,0.05,华东,直播带货,微信支付,4.3,2
+ORD202400462,2024-09-11,服装鞋帽,T恤,80.01,3,240.03,0.0,东北,线上商城,微信支付,3.4,5
+ORD202400463,2024-01-17,家居用品,保温杯,72.5,1,65.25,0.1,华东,直播带货,微信支付,4.6,4
+ORD202400464,2024-08-31,服装鞋帽,运动鞋,340.68,1,306.61,0.1,华南,线上商城,微信支付,4.1,3
+ORD202400465,2024-07-06,家居用品,台灯,77.23,4,293.47,0.05,华北,线上商城,货到付款,5.0,2
+ORD202400466,2024-02-13,食品饮料,坚果礼盒,97.74,1,97.74,0.0,华东,线上商城,微信支付,4.1,3
+ORD202400467,2024-10-28,电子产品,智能手表,1059.65,1,1059.65,0.0,西南,线上商城,微信支付,5.0,3
+ORD202400468,2024-07-05,食品饮料,咖啡豆,74.93,3,224.79,0.0,西南,线上商城,支付宝,3.7,6
+ORD202400469,2024-02-17,美妆个护,防晒霜,83.45,1,83.45,0.0,华南,线上商城,微信支付,4.7,3
+ORD202400470,2024-12-05,服装鞋帽,运动鞋,331.26,1,265.01,0.2,华北,直播带货,支付宝,4.0,4
+ORD202400471,2024-08-27,家居用品,台灯,98.34,3,265.52,0.1,华中,直播带货,支付宝,4.6,4
+ORD202400472,2024-08-10,服装鞋帽,羽绒服,540.89,3,1541.54,0.05,西南,直播带货,支付宝,4.2,3
+ORD202400473,2024-06-07,服装鞋帽,帽子,63.11,1,63.11,0.0,华东,线上商城,支付宝,4.6,2
+ORD202400474,2024-09-05,美妆个护,面膜礼盒,144.02,1,136.82,0.05,华中,线下门店,微信支付,5.0,2
+ORD202400475,2024-03-12,家居用品,台灯,96.33,4,366.05,0.05,华北,线上商城,微信支付,4.1,3
+ORD202400476,2024-04-30,食品饮料,咖啡豆,67.0,1,63.65,0.05,华北,线上商城,微信支付,5.0,2
+ORD202400477,2024-12-15,家居用品,保温杯,72.4,4,260.64,0.1,华北,线上商城,支付宝,3.9,1
+ORD202400478,2024-01-28,美妆个护,洗发水,55.55,1,52.77,0.05,华中,直播带货,微信支付,,4
+ORD202400479,2024-05-10,家居用品,吸尘器,704.42,3,2113.26,0.0,华中,直播带货,银行卡,3.6,4
+ORD202400480,2024-09-08,服装鞋帽,牛仔裤,203.03,1,182.73,0.1,华中,线下门店,银行卡,4.0,3
+ORD202400481,2024-06-16,服装鞋帽,羽绒服,560.87,2,1065.65,0.05,华南,直播带货,支付宝,5.0,3
+ORD202400482,2024-09-21,美妆个护,洗发水,66.25,2,125.88,0.05,华北,线下门店,支付宝,5.0,3
+ORD202400483,2024-07-07,家居用品,记忆枕,170.01,3,510.03,0.0,华东,线上商城,微信支付,4.8,2
+ORD202400484,2024-03-27,电子产品,笔记本电脑,5676.29,1,5676.29,0.0,东北,线上商城,微信支付,3.7,5
+ORD202400485,2024-09-10,电子产品,蓝牙耳机,235.86,3,672.2,0.05,华东,线上商城,支付宝,4.0,3
+ORD202400486,2024-08-02,家居用品,吸尘器,643.97,5,3219.85,0.0,华南,线下门店,支付宝,,3
+ORD202400487,2024-12-20,服装鞋帽,运动鞋,361.76,2,578.82,0.2,华东,线上商城,支付宝,5.0,2
+ORD202400488,2024-11-02,美妆个护,面膜礼盒,110.71,1,94.1,0.15,华东,线上商城,支付宝,4.2,3
+ORD202400489,2024-06-22,食品饮料,咖啡豆,65.77,2,111.81,0.15,华东,线上商城,支付宝,5.0,3
+ORD202400490,2024-05-24,食品饮料,红茶礼盒,113.73,2,216.09,0.05,华北,线上商城,支付宝,4.4,2
+ORD202400491,2024-11-21,美妆个护,面膜礼盒,117.87,1,111.98,0.05,华东,线上商城,微信支付,5.0,1
+ORD202400492,2024-12-29,服装鞋帽,牛仔裤,179.62,3,511.92,0.05,华东,线上商城,银行卡,5.0,2
+ORD202400493,2024-10-26,家居用品,空气炸锅,382.97,2,689.35,0.1,华中,线上商城,微信支付,4.3,4
+ORD202400494,2024-07-10,服装鞋帽,羽绒服,572.57,1,572.57,0.0,华北,直播带货,微信支付,4.3,3
+ORD202400495,2024-11-14,食品饮料,红茶礼盒,124.76,5,499.04,0.2,华东,线下门店,微信支付,5.0,2
+ORD202400496,2024-02-17,服装鞋帽,运动鞋,313.49,3,846.42,0.1,华东,线下门店,支付宝,4.6,2
+ORD202400497,2024-09-08,食品饮料,红茶礼盒,123.32,2,234.31,0.05,华南,线下门店,微信支付,5.0,1
+ORD202400498,2024-08-04,食品饮料,进口零食包,59.79,2,119.58,0.0,华东,线上商城,微信支付,5.0,2
+ORD202400499,2024-09-25,电子产品,充电宝,117.74,4,423.86,0.1,华东,线下门店,微信支付,5.0,2
+ORD202400500,2024-01-24,服装鞋帽,帽子,53.76,1,51.07,0.05,华北,直播带货,微信支付,4.2,4
+ORD202400501,2024-01-24,电子产品,智能手表,1038.6,2,1869.48,0.1,华南,线上商城,货到付款,4.3,3
+ORD202400502,2024-12-10,电子产品,笔记本电脑,4961.17,1,4961.17,0.0,华东,线上商城,微信支付,3.4,3
+ORD202400503,2024-11-11,家居用品,记忆枕,163.71,1,155.52,0.05,华东,线上商城,支付宝,3.7,4
+ORD202400504,2024-08-11,食品饮料,进口零食包,59.88,1,53.89,0.1,华东,线上商城,支付宝,4.1,3
+ORD202400505,2024-08-18,服装鞋帽,运动鞋,319.07,3,861.49,0.1,华东,线上商城,微信支付,3.6,1
+ORD202400506,2024-10-10,食品饮料,坚果礼盒,87.67,1,78.9,0.1,华东,线上商城,支付宝,4.7,1
+ORD202400507,2024-02-29,服装鞋帽,T恤,77.78,1,77.78,0.0,华东,线上商城,支付宝,5.0,2
+ORD202400508,2024-12-02,服装鞋帽,T恤,84.28,1,71.64,0.15,西南,直播带货,微信支付,2.6,5
+ORD202400509,2024-02-14,美妆个护,洗发水,54.12,1,48.71,0.1,华东,线上商城,货到付款,5.0,2
+ORD202400510,2024-07-01,美妆个护,洗发水,65.9,4,263.6,0.0,西南,线上商城,支付宝,4.7,4
+ORD202400511,2024-01-24,美妆个护,防晒霜,88.37,1,88.37,0.0,华南,线下门店,微信支付,5.0,2
+ORD202400512,2024-02-22,食品饮料,坚果礼盒,88.19,1,88.19,0.0,华中,线下门店,支付宝,5.0,3
+ORD202400513,2024-11-27,家居用品,吸尘器,665.17,1,565.39,0.15,西南,线上商城,银行卡,4.4,3
+ORD202400514,2024-07-31,家居用品,吸尘器,671.61,2,1208.9,0.1,西南,线上商城,微信支付,3.1,5
+ORD202400515,2024-01-16,服装鞋帽,羽绒服,553.95,1,553.95,0.0,华东,线上商城,货到付款,5.0,2
+ORD202400516,2024-10-21,电子产品,笔记本电脑,6622.43,1,6291.31,0.05,华南,线上商城,支付宝,3.1,4
+ORD202400517,2024-11-25,家居用品,台灯,93.36,2,158.71,0.15,华南,线下门店,微信支付,4.5,3
+ORD202400518,2024-03-05,服装鞋帽,帽子,51.43,1,48.86,0.05,华北,直播带货,支付宝,3.8,5
+ORD202400519,2024-08-04,食品饮料,坚果礼盒,94.12,3,268.24,0.05,华南,线上商城,支付宝,4.3,3
+ORD202400520,2024-04-30,食品饮料,进口零食包,67.3,4,269.2,0.0,华北,线上商城,微信支付,4.3,2
+ORD202400521,2024-06-16,服装鞋帽,运动鞋,372.93,1,372.93,0.0,华东,线上商城,微信支付,4.8,3
+ORD202400522,2024-12-12,服装鞋帽,帽子,55.97,3,151.12,0.1,华南,直播带货,货到付款,3.2,3
+ORD202400523,2024-09-15,电子产品,充电宝,106.66,2,213.32,0.0,华南,线上商城,支付宝,4.6,4
+ORD202400524,2024-02-26,电子产品,笔记本电脑,5433.89,1,5433.89,0.0,华北,线下门店,支付宝,5.0,1
+ORD202400525,2024-06-05,食品饮料,咖啡豆,67.87,2,135.74,0.0,华东,线上商城,银行卡,5.0,1
+ORD202400526,2024-08-31,食品饮料,进口零食包,58.73,1,52.86,0.1,西南,直播带货,支付宝,3.9,4
+ORD202400527,2024-07-21,家居用品,空气炸锅,432.22,4,1642.44,0.05,华南,线下门店,微信支付,4.6,2
+ORD202400528,2024-10-13,电子产品,笔记本电脑,5872.79,2,11745.58,0.0,西南,线上商城,支付宝,4.0,3
+ORD202400529,2024-12-27,食品饮料,进口零食包,64.9,3,155.76,0.2,华东,直播带货,微信支付,4.2,3
+ORD202400530,2024-07-14,家居用品,空气炸锅,434.7,1,412.96,0.05,华东,线上商城,支付宝,5.0,2
+ORD202400531,2024-01-14,美妆个护,香水,406.13,1,365.52,0.1,华中,线上商城,货到付款,4.5,3
+ORD202400532,2024-09-17,美妆个护,电动牙刷,261.01,1,247.96,0.05,华北,线上商城,微信支付,5.0,2
+ORD202400533,2024-12-11,电子产品,笔记本电脑,6063.7,1,4850.96,0.2,华北,线上商城,微信支付,5.0,3
+ORD202400534,2024-10-02,食品饮料,进口零食包,65.04,1,58.54,0.1,东北,直播带货,微信支付,3.8,5
+ORD202400535,2024-12-14,服装鞋帽,T恤,87.12,1,69.7,0.2,华东,线上商城,微信支付,4.9,2
+ORD202400536,2024-05-23,食品饮料,咖啡豆,77.49,4,294.46,0.05,华中,线下门店,微信支付,4.0,4
+ORD202400537,2024-04-17,服装鞋帽,帽子,52.17,4,208.68,0.0,华南,线上商城,微信支付,3.7,4
+ORD202400538,2024-07-20,服装鞋帽,羽绒服,526.03,4,1893.71,0.1,华南,线上商城,微信支付,4.1,3
+ORD202400539,2024-01-05,电子产品,智能手表,1243.96,1,1243.96,0.0,西南,直播带货,微信支付,3.8,4
+ORD202400540,2024-04-23,家居用品,记忆枕,171.03,1,153.93,0.1,华中,直播带货,支付宝,5.0,4
+ORD202400541,2024-09-09,家居用品,吸尘器,785.96,2,1571.92,0.0,华东,线上商城,支付宝,5.0,2
+ORD202400542,2024-04-08,食品饮料,坚果礼盒,100.99,2,191.88,0.05,华南,线下门店,支付宝,5.0,2
+ORD202400543,2024-07-27,家居用品,台灯,89.1,1,89.1,0.0,华中,线上商城,支付宝,,2
+ORD202400544,2024-10-19,食品饮料,咖啡豆,85.76,1,85.76,0.0,华中,线上商城,支付宝,4.4,4
+ORD202400545,2024-11-16,食品饮料,红茶礼盒,101.66,1,91.49,0.1,西南,线下门店,微信支付,4.1,4
+ORD202400546,2024-08-16,服装鞋帽,羽绒服,594.03,4,2138.51,0.1,华东,线下门店,支付宝,5.0,3
+ORD202400547,2024-02-03,电子产品,智能手机,3256.89,1,2931.2,0.1,华北,直播带货,微信支付,3.4,4
+ORD202400548,2024-08-07,食品饮料,进口零食包,58.11,2,116.22,0.0,华北,线上商城,支付宝,5.0,3
+ORD202400549,2024-10-20,电子产品,笔记本电脑,5375.94,1,4838.35,0.1,华东,线上商城,支付宝,5.0,3
+ORD202400550,2024-10-25,食品饮料,咖啡豆,67.07,1,63.72,0.05,华东,线下门店,微信支付,4.8,2
+ORD202400551,2024-01-28,服装鞋帽,运动鞋,388.6,2,777.2,0.0,西南,线上商城,微信支付,3.2,3
+ORD202400552,2024-06-20,服装鞋帽,牛仔裤,170.75,1,170.75,0.0,华北,线下门店,微信支付,4.5,2
+ORD202400553,2024-06-18,食品饮料,橄榄油,89.34,1,89.34,0.0,华北,线上商城,支付宝,4.8,3
+ORD202400554,2024-01-23,食品饮料,咖啡豆,68.2,1,64.79,0.05,华南,线上商城,微信支付,4.6,3
+ORD202400555,2024-12-12,电子产品,智能手机,3044.85,1,2588.12,0.15,华东,直播带货,微信支付,3.0,5
+ORD202400556,2024-02-03,电子产品,蓝牙耳机,283.0,1,254.7,0.1,华南,线上商城,微信支付,4.1,2
+ORD202400557,2024-05-30,美妆个护,香水,388.14,2,737.47,0.05,华中,线下门店,货到付款,4.3,3
+ORD202400558,2024-02-03,家居用品,记忆枕,161.31,2,306.49,0.05,华北,线上商城,支付宝,5.0,2
+ORD202400559,2024-07-24,美妆个护,防晒霜,99.27,1,99.27,0.0,华北,线上商城,支付宝,5.0,2
+ORD202400560,2024-02-13,电子产品,智能手机,3003.25,1,3003.25,0.0,华南,线上商城,微信支付,5.0,2
+ORD202400561,2024-07-14,电子产品,充电宝,110.42,2,209.8,0.05,华中,线上商城,微信支付,4.5,3
+ORD202400562,2024-07-03,电子产品,蓝牙耳机,247.03,2,469.36,0.05,华东,直播带货,微信支付,5.0,2
+ORD202400563,2024-10-05,食品饮料,坚果礼盒,95.13,1,90.37,0.05,西南,线上商城,微信支付,4.6,2
+ORD202400564,2024-03-17,食品饮料,咖啡豆,68.86,2,123.95,0.1,华东,直播带货,微信支付,5.0,1
+ORD202400565,2024-08-07,美妆个护,电动牙刷,209.96,1,209.96,0.0,华北,线上商城,支付宝,4.5,3
+ORD202400566,2024-08-02,服装鞋帽,牛仔裤,194.19,2,368.96,0.05,华东,线上商城,微信支付,4.9,2
+ORD202400567,2024-11-04,食品饮料,进口零食包,73.27,5,348.03,0.05,东北,线上商城,微信支付,4.4,3
+ORD202400568,2024-03-28,电子产品,笔记本电脑,5239.09,3,14145.54,0.1,华东,线上商城,银行卡,5.0,2
+ORD202400569,2024-07-07,服装鞋帽,运动鞋,322.33,4,1160.39,0.1,华东,线上商城,微信支付,5.0,2
+ORD202400570,2024-04-15,家居用品,记忆枕,134.65,2,255.84,0.05,华南,线上商城,微信支付,4.6,3
+ORD202400571,2024-05-06,服装鞋帽,帽子,61.26,1,61.26,0.0,华南,线上商城,银行卡,5.0,2
+ORD202400572,2024-03-30,服装鞋帽,运动鞋,380.8,2,685.44,0.1,华北,直播带货,微信支付,4.5,4
+ORD202400573,2024-11-17,服装鞋帽,羽绒服,550.89,2,1046.69,0.05,华中,线上商城,微信支付,4.2,4
+ORD202400574,2024-07-28,服装鞋帽,T恤,72.06,1,72.06,0.0,华东,直播带货,微信支付,5.0,2
+ORD202400575,2024-02-28,美妆个护,洗发水,56.89,2,102.4,0.1,华北,线上商城,微信支付,4.5,2
+ORD202400576,2024-01-10,服装鞋帽,运动鞋,319.0,1,287.1,0.1,华东,直播带货,支付宝,5.0,2
+ORD202400577,2024-09-29,家居用品,空气炸锅,418.36,3,1255.08,0.0,华北,线上商城,支付宝,4.1,4
+ORD202400578,2024-05-02,家居用品,空气炸锅,371.07,5,1762.58,0.05,西南,直播带货,支付宝,4.0,4
+ORD202400579,2024-01-23,服装鞋帽,羽绒服,569.24,1,569.24,0.0,华南,线下门店,微信支付,5.0,2
+ORD202400580,2024-11-30,家居用品,记忆枕,137.07,1,123.36,0.1,华南,线上商城,支付宝,4.8,3
+ORD202400581,2024-10-01,美妆个护,防晒霜,85.64,4,325.43,0.05,西北,直播带货,微信支付,3.1,6
+ORD202400582,2024-06-11,食品饮料,橄榄油,85.28,1,68.22,0.2,华北,线上商城,微信支付,,1
+ORD202400583,2024-03-04,家居用品,记忆枕,161.58,2,323.16,0.0,华东,线上商城,支付宝,5.0,1
+ORD202400584,2024-08-15,美妆个护,香水,373.92,1,373.92,0.0,华北,线下门店,微信支付,5.0,2
+ORD202400585,2024-10-03,电子产品,蓝牙耳机,283.51,2,567.02,0.0,华南,线上商城,支付宝,4.6,3
+ORD202400586,2024-11-20,食品饮料,咖啡豆,66.58,1,53.26,0.2,西南,线下门店,支付宝,3.8,5
+ORD202400587,2024-03-31,服装鞋帽,T恤,73.91,1,73.91,0.0,华东,直播带货,支付宝,4.5,3
+ORD202400588,2024-08-09,美妆个护,电动牙刷,196.87,1,196.87,0.0,西南,线下门店,微信支付,4.8,4
+ORD202400589,2024-07-11,服装鞋帽,牛仔裤,156.88,1,141.19,0.1,华东,线下门店,支付宝,3.9,3
+ORD202400590,2024-07-21,食品饮料,红茶礼盒,106.17,1,106.17,0.0,华北,线上商城,货到付款,5.0,2
+ORD202400591,2024-06-27,服装鞋帽,牛仔裤,187.29,1,187.29,0.0,华南,线下门店,微信支付,4.7,3
+ORD202400592,2024-12-23,美妆个护,洗发水,55.7,1,52.91,0.05,华东,直播带货,货到付款,4.3,3
+ORD202400593,2024-07-22,美妆个护,面膜礼盒,130.72,1,117.65,0.1,华东,线上商城,微信支付,5.0,1
+ORD202400594,2024-02-15,电子产品,笔记本电脑,6281.44,1,6281.44,0.0,华东,线上商城,微信支付,4.9,2
+ORD202400595,2024-12-22,服装鞋帽,牛仔裤,188.34,2,357.85,0.05,华东,线上商城,支付宝,4.9,1
+ORD202400596,2024-04-12,电子产品,蓝牙耳机,284.12,1,255.71,0.1,华北,线下门店,支付宝,4.8,2
+ORD202400597,2024-01-16,服装鞋帽,牛仔裤,179.2,2,358.4,0.0,华东,直播带货,支付宝,4.3,1
+ORD202400598,2024-11-04,服装鞋帽,T恤,89.24,1,80.32,0.1,华南,线上商城,微信支付,4.7,3
+ORD202400599,2024-11-08,电子产品,智能手表,1223.12,3,3669.36,0.0,华北,直播带货,微信支付,4.2,3
+ORD202400600,2024-12-24,美妆个护,洗发水,57.63,2,97.97,0.15,华北,直播带货,支付宝,3.9,2
+ORD202400601,2024-06-15,美妆个护,洗发水,61.92,1,49.54,0.2,西南,线上商城,货到付款,3.9,4
+ORD202400602,2024-07-16,家居用品,吸尘器,797.69,3,2393.07,0.0,华东,直播带货,支付宝,4.4,2
+ORD202400603,2024-02-29,服装鞋帽,帽子,50.72,1,50.72,0.0,华东,线下门店,支付宝,4.4,3
+ORD202400604,2024-12-06,服装鞋帽,羽绒服,545.81,4,1855.75,0.15,华东,直播带货,银行卡,3.5,4
+ORD202400605,2024-07-14,美妆个护,防晒霜,108.29,1,108.29,0.0,华东,线上商城,支付宝,5.0,3
+ORD202400606,2024-07-07,电子产品,智能手表,1249.84,2,2499.68,0.0,华东,直播带货,支付宝,5.0,2
+ORD202400607,2024-03-05,服装鞋帽,帽子,56.5,1,56.5,0.0,华北,线上商城,支付宝,3.8,4
+ORD202400608,2024-03-15,美妆个护,面膜礼盒,133.86,1,133.86,0.0,华东,线下门店,微信支付,4.7,2
+ORD202400609,2024-06-19,电子产品,蓝牙耳机,287.15,4,1091.17,0.05,华南,线上商城,银行卡,5.0,3
+ORD202400610,2024-03-22,食品饮料,进口零食包,63.87,1,57.48,0.1,西北,直播带货,微信支付,4.0,4
+ORD202400611,2024-07-20,电子产品,智能手表,1183.25,2,2366.5,0.0,西南,直播带货,微信支付,4.3,4
+ORD202400612,2024-04-21,食品饮料,咖啡豆,80.29,1,80.29,0.0,华南,线下门店,支付宝,3.9,4
+ORD202400613,2024-08-30,家居用品,空气炸锅,336.38,1,319.56,0.05,华南,线下门店,微信支付,4.6,3
+ORD202400614,2024-03-29,美妆个护,电动牙刷,249.03,5,1245.15,0.0,西南,线下门店,微信支付,5.0,3
+ORD202400615,2024-09-07,美妆个护,防晒霜,87.16,2,156.89,0.1,华东,直播带货,微信支付,5.0,3
+ORD202400616,2024-02-20,家居用品,台灯,81.49,3,244.47,0.0,华北,直播带货,微信支付,4.5,3
+ORD202400617,2024-11-29,电子产品,蓝牙耳机,296.32,4,1126.02,0.05,华中,直播带货,支付宝,4.5,3
+ORD202400618,2024-02-14,服装鞋帽,羽绒服,455.2,1,432.44,0.05,华北,线上商城,微信支付,4.5,3
+ORD202400619,2024-05-30,美妆个护,电动牙刷,205.15,1,194.89,0.05,华北,线下门店,支付宝,4.9,2
+ORD202400620,2024-04-06,电子产品,蓝牙耳机,295.68,2,591.36,0.0,华南,线上商城,微信支付,5.0,2
+ORD202400621,2024-08-21,电子产品,智能手表,1158.25,4,4633.0,0.0,华北,线上商城,微信支付,4.1,4
+ORD202400622,2024-09-18,服装鞋帽,牛仔裤,197.36,1,177.62,0.1,华北,线下门店,支付宝,5.0,2
+ORD202400623,2024-02-15,家居用品,保温杯,70.42,1,70.42,0.0,华东,线上商城,货到付款,5.0,2
+ORD202400624,2024-06-05,服装鞋帽,牛仔裤,158.16,1,126.53,0.2,华中,线上商城,微信支付,4.4,3
+ORD202400625,2024-12-13,食品饮料,进口零食包,67.02,3,170.9,0.15,华北,线上商城,支付宝,4.8,2
+ORD202400626,2024-01-28,食品饮料,红茶礼盒,116.85,1,105.16,0.1,华南,线上商城,货到付款,4.0,3
+ORD202400627,2024-04-27,家居用品,空气炸锅,431.01,1,409.46,0.05,西南,直播带货,支付宝,4.3,4
+ORD202400628,2024-09-09,食品饮料,坚果礼盒,100.2,1,100.2,0.0,华北,线上商城,货到付款,4.0,3
+ORD202400629,2024-01-04,美妆个护,防晒霜,102.12,1,102.12,0.0,华南,线下门店,微信支付,3.7,4
+ORD202400630,2024-09-20,家居用品,空气炸锅,371.19,1,352.63,0.05,东北,线上商城,微信支付,2.8,5
+ORD202400631,2024-05-15,家居用品,台灯,87.13,4,348.52,0.0,华中,线下门店,银行卡,4.6,3
+ORD202400632,2024-06-28,食品饮料,咖啡豆,72.8,1,61.88,0.15,华北,线上商城,微信支付,4.8,3
+ORD202400633,2024-04-25,服装鞋帽,T恤,74.24,50,3712.0,0.0,华北,线上商城,货到付款,4.6,2
+ORD202400634,2024-12-29,家居用品,吸尘器,701.6,1,596.36,0.15,西南,线下门店,支付宝,4.4,4
+ORD202400635,2024-10-08,服装鞋帽,运动鞋,322.14,2,579.85,0.1,华北,线下门店,银行卡,5.0,2
+ORD202400636,2024-09-13,电子产品,蓝牙耳机,225.06,3,607.66,0.1,东北,直播带货,微信支付,4.7,4
+ORD202400637,2024-09-18,家居用品,保温杯,70.2,3,210.6,0.0,华南,线上商城,微信支付,4.3,3
+ORD202400638,2024-10-29,美妆个护,洗发水,55.13,1,55.13,0.0,华东,线上商城,支付宝,4.6,3
+ORD202400639,2024-12-24,美妆个护,洗发水,54.99,1,54.99,0.0,华北,直播带货,支付宝,4.8,3
+ORD202400640,2024-01-25,家居用品,空气炸锅,328.94,2,657.88,0.0,华东,线上商城,微信支付,5.0,1
+ORD202400641,2024-12-08,电子产品,笔记本电脑,6270.61,1,5643.55,0.1,西南,线上商城,微信支付,4.3,3
+ORD202400642,2024-02-24,电子产品,笔记本电脑,5139.42,2,10278.84,0.0,华东,线上商城,支付宝,4.3,3
+ORD202400643,2024-09-30,美妆个护,面膜礼盒,115.49,1,109.72,0.05,华中,直播带货,银行卡,4.4,3
+ORD202400644,2024-08-11,电子产品,充电宝,120.86,4,483.44,0.0,华东,线上商城,货到付款,5.0,2
+ORD202400645,2024-07-22,服装鞋帽,帽子,61.11,5,305.55,,华北,线上商城,微信支付,5.0,2
+ORD202400646,2024-10-30,美妆个护,防晒霜,93.73,2,178.09,0.05,华东,线上商城,微信支付,4.9,2
+ORD202400647,2024-03-16,食品饮料,红茶礼盒,124.1,1,124.1,0.0,华南,线上商城,银行卡,4.5,3
+ORD202400648,2024-02-12,食品饮料,进口零食包,61.33,1,58.26,0.05,华东,线上商城,微信支付,4.7,2
+ORD202400649,2024-10-02,美妆个护,防晒霜,83.78,2,150.8,0.1,东北,线上商城,微信支付,3.9,4
+ORD202400650,2024-11-18,食品饮料,橄榄油,87.88,1,74.7,0.15,华北,线下门店,支付宝,4.6,3
+ORD202400651,2024-04-02,美妆个护,面膜礼盒,122.6,3,331.02,0.1,华南,线上商城,支付宝,5.0,2
+ORD202400652,2024-03-11,美妆个护,面膜礼盒,134.03,1,134.03,0.0,华东,线上商城,银行卡,4.9,2
+ORD202400653,2024-11-03,电子产品,智能手机,3639.31,1,3275.38,0.1,西南,线上商城,支付宝,2.8,4
+ORD202400654,2024-05-04,电子产品,智能手表,1142.07,1,1142.07,0.0,华东,线上商城,微信支付,4.8,2
+ORD202400655,2024-03-16,家居用品,保温杯,59.65,3,161.06,0.1,华南,直播带货,银行卡,5.0,1
+ORD202400656,2024-02-09,服装鞋帽,羽绒服,498.24,4,1992.96,0.0,华中,直播带货,支付宝,4.4,3
+ORD202400657,2024-06-03,美妆个护,香水,400.9,1,340.76,0.15,华南,线下门店,微信支付,,3
+ORD202400658,2024-12-01,电子产品,蓝牙耳机,251.47,2,427.5,0.15,西南,线上商城,微信支付,4.1,3
+ORD202400659,2024-06-29,家居用品,记忆枕,149.16,5,596.64,0.2,西南,线上商城,微信支付,4.2,3
+ORD202400660,2024-09-29,服装鞋帽,运动鞋,328.23,2,590.81,0.1,华北,直播带货,微信支付,4.8,3
+ORD202400661,2024-07-04,服装鞋帽,运动鞋,382.93,1,363.78,0.05,华北,线下门店,微信支付,5.0,3
+ORD202400662,2024-08-03,家居用品,吸尘器,650.07,1,650.07,0.0,华北,线上商城,货到付款,5.0,2
+ORD202400663,2024-03-21,电子产品,智能手表,981.62,1,981.62,0.0,华南,直播带货,支付宝,4.9,4
+ORD202400664,2024-07-29,美妆个护,电动牙刷,249.42,2,473.9,0.05,华东,线上商城,微信支付,4.8,2
+ORD202400665,2024-08-20,服装鞋帽,T恤,88.93,1,88.93,0.0,华北,线上商城,货到付款,4.8,1
+ORD202400666,2024-04-22,服装鞋帽,帽子,56.73,1,51.06,0.1,华北,直播带货,微信支付,4.7,3
+ORD202400667,2024-02-23,电子产品,笔记本电脑,6285.11,1,6285.11,0.0,华东,线上商城,支付宝,4.5,1
+ORD202400668,2024-01-17,家居用品,空气炸锅,434.34,2,781.81,0.1,华东,线上商城,货到付款,4.6,3
+ORD202400669,2024-07-19,美妆个护,洗发水,63.65,1,57.28,0.1,华中,线上商城,微信支付,4.4,3
+ORD202400670,2024-01-20,家居用品,吸尘器,791.03,1,791.03,0.0,东北,线上商城,支付宝,3.8,4
+ORD202400671,2024-11-07,美妆个护,香水,413.38,1,372.04,0.1,华北,线下门店,微信支付,3.4,4
+ORD202400672,2024-02-08,食品饮料,进口零食包,68.53,1,68.53,0.0,华东,线下门店,微信支付,4.4,3
+ORD202400673,2024-05-31,家居用品,保温杯,66.49,1,66.49,0.0,华东,直播带货,支付宝,5.0,2
+ORD202400674,2024-03-04,食品饮料,橄榄油,84.46,3,228.04,0.1,华北,直播带货,微信支付,5.0,2
+ORD202400675,2024-07-30,服装鞋帽,T恤,87.94,5,439.7,0.0,华南,直播带货,微信支付,,4
+ORD202400676,2024-03-22,服装鞋帽,羽绒服,476.8,3,1430.4,0.0,华北,线上商城,微信支付,4.3,3
+ORD202400677,2024-07-22,美妆个护,洗发水,58.23,1,55.32,0.05,西北,线上商城,微信支付,4.2,4
+ORD202400678,2024-11-23,家居用品,台灯,77.35,2,131.49,0.15,华东,直播带货,微信支付,4.3,3
+ORD202400679,2024-08-04,家居用品,吸尘器,656.31,1,590.68,0.1,华东,线下门店,微信支付,5.0,2
+ORD202400680,2024-08-07,家居用品,保温杯,58.46,1,52.61,0.1,华北,线上商城,微信支付,4.4,3
+ORD202400681,2024-10-14,美妆个护,防晒霜,86.41,2,172.82,0.0,华东,线下门店,货到付款,4.2,2
+ORD202400682,2024-03-16,服装鞋帽,T恤,69.84,5,314.28,0.1,华东,直播带货,货到付款,3.8,4
+ORD202400683,2024-01-19,家居用品,吸尘器,732.04,1,732.04,0.0,华南,线上商城,支付宝,5.0,3
+ORD202400684,2024-06-03,食品饮料,进口零食包,72.32,1,61.47,0.15,华南,线上商城,微信支付,5.0,2
+ORD202400685,2024-03-31,服装鞋帽,T恤,71.34,2,128.41,0.1,华北,线下门店,微信支付,5.0,2
+ORD202400686,2024-02-14,食品饮料,坚果礼盒,77.17,1,69.45,0.1,华东,直播带货,微信支付,4.8,2
+ORD202400687,2024-08-03,家居用品,记忆枕,152.32,1,137.09,0.1,东北,线上商城,微信支付,5.0,2
+ORD202400688,2024-07-10,美妆个护,防晒霜,88.83,3,253.17,0.05,华东,线下门店,银行卡,5.0,2
+ORD202400689,2024-07-10,食品饮料,进口零食包,72.34,2,137.45,0.05,华东,线上商城,微信支付,4.8,1
+ORD202400690,2024-10-22,服装鞋帽,牛仔裤,205.78,1,195.49,0.05,华北,线上商城,支付宝,4.0,3
+ORD202400691,2024-11-17,食品饮料,进口零食包,59.25,1,59.25,0.0,华东,线上商城,微信支付,4.7,2
+ORD202400692,2024-07-14,电子产品,蓝牙耳机,297.71,2,565.65,0.05,华东,直播带货,支付宝,5.0,3
+ORD202400693,2024-05-13,美妆个护,香水,367.45,1,367.45,0.0,华南,线上商城,支付宝,5.0,2
+ORD202400694,2024-02-17,电子产品,智能手表,1236.2,1,1236.2,0.0,华北,线上商城,支付宝,4.5,3
+ORD202400695,2024-08-18,食品饮料,咖啡豆,70.47,4,281.88,0.0,华东,线上商城,支付宝,,3
+ORD202400696,2024-03-03,家居用品,空气炸锅,384.1,1,384.1,0.0,华东,线上商城,银行卡,5.0,2
+ORD202400697,2024-05-20,服装鞋帽,羽绒服,496.37,2,943.1,0.05,华北,直播带货,微信支付,3.3,5
+ORD202400698,2024-05-23,食品饮料,坚果礼盒,90.21,2,171.4,0.05,华东,线上商城,微信支付,5.0,1
+ORD202400699,2024-02-25,服装鞋帽,牛仔裤,163.78,1,163.78,0.0,华北,线下门店,货到付款,5.0,2
+ORD202400700,2024-09-28,家居用品,空气炸锅,336.07,3,907.39,0.1,西南,线上商城,支付宝,4.1,3
+ORD202400701,2024-01-06,美妆个护,洗发水,59.38,3,178.14,0.0,华南,线上商城,微信支付,4.5,3
+ORD202400702,2024-12-09,电子产品,充电宝,126.56,1,113.9,0.1,华东,线上商城,微信支付,4.5,3
+ORD202400703,2024-10-29,服装鞋帽,帽子,47.1,1,47.1,0.0,华中,线上商城,银行卡,5.0,2
+ORD202400704,2024-02-05,服装鞋帽,牛仔裤,192.06,1,182.46,0.05,华北,线上商城,支付宝,4.7,2
+ORD202400705,2024-03-03,食品饮料,进口零食包,58.13,1,58.13,0.0,华东,线下门店,银行卡,4.6,2
+ORD202400706,2024-10-02,食品饮料,坚果礼盒,81.66,3,244.98,0.0,华东,线上商城,支付宝,4.2,1
+ORD202400707,2024-10-25,服装鞋帽,运动鞋,321.57,1,289.41,0.1,华南,直播带货,微信支付,4.7,4
+ORD202400708,2024-09-04,家居用品,保温杯,70.19,2,133.36,0.05,华北,线下门店,微信支付,4.7,2
+ORD202400709,2024-01-07,美妆个护,电动牙刷,251.98,1,251.98,0.0,华北,直播带货,微信支付,5.0,2
+ORD202400710,2024-09-15,电子产品,蓝牙耳机,238.07,1,214.26,0.1,华中,线上商城,微信支付,4.9,3
+ORD202400711,2024-11-27,电子产品,充电宝,133.54,2,253.73,0.05,华东,线上商城,支付宝,4.9,1
+ORD202400712,2024-06-30,服装鞋帽,运动鞋,329.49,1,296.54,0.1,华东,线上商城,支付宝,4.3,1
+ORD202400713,2024-10-31,家居用品,台灯,89.83,1,89.83,0.0,华南,线下门店,支付宝,4.6,3
+ORD202400714,2024-04-25,食品饮料,橄榄油,94.82,1,90.08,,华北,线上商城,支付宝,4.4,2
+ORD202400715,2024-12-24,美妆个护,香水,464.42,2,882.4,0.05,华东,线上商城,支付宝,,2
+ORD202400716,2024-03-31,食品饮料,坚果礼盒,79.31,2,158.62,0.0,华南,线下门店,微信支付,5.0,2
+ORD202400717,2024-05-28,食品饮料,进口零食包,66.06,2,132.12,0.0,华东,直播带货,支付宝,5.0,2
+ORD202400718,2024-08-22,电子产品,智能手表,1034.37,1,930.93,0.1,华东,直播带货,微信支付,3.8,5
+ORD202400719,2024-09-11,电子产品,笔记本电脑,6219.36,5,29541.96,0.05,华东,线上商城,货到付款,5.0,2
+ORD202400720,2024-08-18,食品饮料,进口零食包,59.56,4,226.33,0.05,华中,线上商城,微信支付,4.1,4
+ORD202400721,2024-11-09,电子产品,充电宝,122.4,3,330.48,0.1,华东,线下门店,微信支付,4.3,2
+ORD202400722,2024-12-06,家居用品,记忆枕,140.47,3,421.41,0.0,华北,线上商城,银行卡,,3
+ORD202400723,2024-01-26,家居用品,保温杯,72.39,3,217.17,0.0,东北,直播带货,支付宝,3.1,5
+ORD202400724,2024-06-30,家居用品,记忆枕,146.52,5,732.6,0.0,华中,线下门店,微信支付,5.0,3
+ORD202400725,2024-04-12,家居用品,记忆枕,152.44,1,152.44,0.0,华东,线下门店,微信支付,5.0,3
+ORD202400726,2024-12-21,家居用品,保温杯,64.38,1,54.72,0.15,东北,线上商城,银行卡,3.6,5
+ORD202400727,2024-05-04,服装鞋帽,T恤,70.17,1,66.66,0.05,华东,线下门店,支付宝,4.5,2
+ORD202400728,2024-02-13,电子产品,智能手机,3040.14,1,2888.13,0.05,华南,直播带货,银行卡,4.6,4
+ORD202400729,2024-09-21,电子产品,充电宝,106.91,200,21382.0,0.0,华东,线上商城,货到付款,4.6,3
+ORD202400730,2024-11-05,食品饮料,进口零食包,58.02,1,49.32,0.15,华东,线上商城,微信支付,4.2,3
+ORD202400731,2024-10-06,家居用品,台灯,94.9,3,284.7,0.0,华北,直播带货,支付宝,3.4,4
+ORD202400732,2024-08-31,服装鞋帽,T恤,82.97,4,298.69,0.1,华北,线上商城,微信支付,4.0,3
+ORD202400733,2024-03-02,服装鞋帽,羽绒服,532.56,1,479.3,0.1,华东,线下门店,货到付款,5.0,1
+ORD202400734,2024-09-14,家居用品,台灯,87.05,1,87.05,0.0,华北,直播带货,支付宝,3.7,3
+ORD202400735,2024-07-20,电子产品,充电宝,115.55,1,115.55,0.0,华南,线下门店,微信支付,4.5,3
+ORD202400736,2024-11-12,食品饮料,坚果礼盒,75.99,3,216.57,0.05,华东,直播带货,微信支付,4.4,2
+ORD202400737,2024-05-01,电子产品,蓝牙耳机,277.8,1,250.02,0.1,华中,线上商城,支付宝,4.3,4
+ORD202400738,2024-01-16,食品饮料,红茶礼盒,97.05,1,97.05,0.0,西北,线上商城,支付宝,4.6,4
+ORD202400739,2024-09-27,食品饮料,咖啡豆,65.69,1,65.69,0.0,华东,直播带货,银行卡,5.0,2
+ORD202400740,2024-11-24,服装鞋帽,运动鞋,373.91,2,635.65,0.15,华北,线上商城,银行卡,3.6,3
+ORD202400741,2024-10-18,电子产品,笔记本电脑,5448.91,1,5448.91,0.0,华东,线上商城,支付宝,4.6,2
+ORD202400742,2024-03-21,食品饮料,坚果礼盒,88.91,1,84.46,0.05,华北,线上商城,微信支付,4.9,3
+ORD202400743,2024-06-22,服装鞋帽,运动鞋,323.12,2,613.93,0.05,华东,直播带货,支付宝,3.4,3
+ORD202400744,2024-10-15,食品饮料,坚果礼盒,83.93,1,75.54,0.1,华南,直播带货,支付宝,4.6,2
+ORD202400745,2024-09-16,家居用品,保温杯,63.66,1,63.66,0.0,华东,线上商城,微信支付,4.4,2
+ORD202400746,2024-10-09,服装鞋帽,牛仔裤,194.35,2,349.83,0.1,华南,线上商城,支付宝,4.6,4
+ORD202400747,2024-01-30,家居用品,空气炸锅,390.1,1,390.1,0.0,西北,线上商城,微信支付,3.5,5
+ORD202400748,2024-06-28,服装鞋帽,运动鞋,390.81,1,332.19,0.15,华北,线上商城,支付宝,5.0,2
+ORD202400749,2024-04-22,服装鞋帽,T恤,89.46,3,254.96,0.05,华中,线上商城,银行卡,5.0,3
+ORD202400750,2024-11-14,服装鞋帽,羽绒服,473.75,1,379.0,0.2,华东,直播带货,支付宝,5.0,2
+ORD202400751,2024-11-28,美妆个护,面膜礼盒,111.33,3,300.59,0.1,华东,线上商城,银行卡,4.6,3
+ORD202400752,2024-07-15,服装鞋帽,运动鞋,309.98,1,278.98,0.1,华北,直播带货,支付宝,3.4,3
+ORD202400753,2024-05-14,食品饮料,进口零食包,60.39,1,54.35,0.1,华中,直播带货,货到付款,3.2,4
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+ORD202400756,2024-06-13,食品饮料,咖啡豆,73.74,1,73.74,0.0,华北,线下门店,微信支付,4.3,3
+ORD202400757,2024-11-08,服装鞋帽,羽绒服,510.71,1,459.64,0.1,华东,线上商城,支付宝,5.0,3
+ORD202400758,2024-04-04,美妆个护,电动牙刷,235.51,1,235.51,0.0,华东,直播带货,支付宝,5.0,3
+ORD202400759,2024-11-03,家居用品,空气炸锅,331.83,1,265.46,0.2,华东,线上商城,微信支付,3.7,3
+ORD202400760,2024-01-13,家居用品,保温杯,61.78,1,58.69,0.05,华北,线下门店,微信支付,4.7,3
+ORD202400761,2024-11-07,家居用品,保温杯,65.21,3,156.5,0.2,西南,直播带货,货到付款,4.8,3
+ORD202400762,2024-11-28,电子产品,智能手机,3640.28,5,14561.12,0.2,华北,线上商城,微信支付,5.0,1
+ORD202400763,2024-08-29,美妆个护,防晒霜,93.32,1,88.65,0.05,华中,线上商城,微信支付,4.0,4
+ORD202400764,2024-08-15,美妆个护,电动牙刷,220.47,1,220.47,0.0,华中,直播带货,微信支付,4.7,3
+ORD202400765,2024-10-02,家居用品,记忆枕,157.83,2,299.88,0.05,西南,线上商城,支付宝,4.9,3
+ORD202400766,2024-04-11,食品饮料,橄榄油,83.76,1,79.57,0.05,西南,直播带货,微信支付,4.2,5
+ORD202400767,2024-02-07,电子产品,笔记本电脑,6528.4,4,24807.92,0.05,华东,直播带货,支付宝,5.0,2
+ORD202400768,2024-02-25,食品饮料,坚果礼盒,76.75,4,276.3,0.1,华中,直播带货,微信支付,5.0,2
+ORD202400769,2024-10-28,食品饮料,进口零食包,57.76,4,219.49,0.05,华南,线上商城,支付宝,4.8,4
+ORD202400770,2024-09-09,家居用品,台灯,90.48,1,81.43,0.1,华东,线上商城,微信支付,5.0,2
+ORD202400771,2024-08-08,服装鞋帽,帽子,61.26,1,58.2,0.05,华东,线上商城,支付宝,5.0,1
+ORD202400772,2024-02-24,食品饮料,红茶礼盒,98.15,1,98.15,0.0,华东,线下门店,货到付款,,1
+ORD202400773,2024-04-14,家居用品,台灯,88.02,1,88.02,0.0,西南,线上商城,支付宝,3.6,3
+ORD202400774,2024-03-11,食品饮料,坚果礼盒,100.37,3,301.11,0.0,华南,线上商城,微信支付,5.0,3
+ORD202400775,2024-06-10,食品饮料,坚果礼盒,86.24,4,275.97,,华东,线下门店,微信支付,4.9,2
+ORD202400776,2024-07-11,食品饮料,咖啡豆,78.9,1,78.9,0.0,华北,线上商城,微信支付,5.0,3
+ORD202400777,2024-04-19,电子产品,智能手机,3191.06,2,6382.12,0.0,华东,直播带货,微信支付,4.7,2
+ORD202400778,2024-06-02,食品饮料,红茶礼盒,115.54,1,98.21,0.15,华南,线上商城,支付宝,5.0,3
+ORD202400779,2024-08-16,家居用品,记忆枕,132.71,1,126.07,0.05,华南,线上商城,微信支付,4.8,4
+ORD202400780,2024-12-17,家居用品,保温杯,60.47,1,51.4,0.15,华东,直播带货,支付宝,5.0,2
+ORD202400781,2024-05-17,服装鞋帽,运动鞋,323.66,3,922.43,0.05,华北,线上商城,银行卡,4.8,1
+ORD202400782,2024-03-07,服装鞋帽,羽绒服,590.24,1,531.22,0.1,华东,直播带货,银行卡,3.9,3
+ORD202400783,2024-04-11,食品饮料,进口零食包,63.93,2,121.47,0.05,西南,线下门店,支付宝,4.9,4
+ORD202400784,2024-09-26,电子产品,充电宝,130.49,1,123.97,0.05,华东,线上商城,微信支付,5.0,2
+ORD202400785,2024-03-08,食品饮料,红茶礼盒,121.95,1,109.76,0.1,华东,直播带货,微信支付,4.4,3
+ORD202400786,2024-06-08,美妆个护,电动牙刷,230.85,1,207.76,0.1,华北,线上商城,货到付款,4.8,2
+ORD202400787,2024-05-22,服装鞋帽,帽子,55.11,1,49.6,0.1,华南,线下门店,微信支付,4.0,3
+ORD202400788,2024-08-16,家居用品,台灯,79.44,1,79.44,0.0,西南,线下门店,支付宝,5.0,4
+ORD202400789,2024-11-11,食品饮料,进口零食包,70.42,2,133.8,0.05,华东,线上商城,微信支付,3.9,2
+ORD202400790,2024-01-03,家居用品,吸尘器,807.38,3,2179.93,0.1,华东,直播带货,支付宝,4.4,2
+ORD202400791,2024-02-23,服装鞋帽,羽绒服,500.61,2,1001.22,0.0,华北,线上商城,支付宝,4.4,3
+ORD202400792,2024-09-25,服装鞋帽,帽子,53.65,1,53.65,0.0,华北,线上商城,货到付款,4.5,2
+ORD202400793,2024-03-13,家居用品,吸尘器,818.13,4,3108.89,0.05,华北,线上商城,微信支付,3.7,4
+ORD202400794,2024-09-25,美妆个护,电动牙刷,196.18,1,196.18,0.0,华北,线下门店,支付宝,4.7,2
+ORD202400795,2024-11-15,服装鞋帽,运动鞋,295.81,2,562.04,0.05,华北,线上商城,支付宝,5.0,2
+ORD202400796,2024-07-25,食品饮料,进口零食包,66.81,2,133.62,0.0,华南,线上商城,支付宝,5.0,3
+ORD202400797,2024-07-15,家居用品,台灯,92.83,5,417.74,0.1,华东,线上商城,支付宝,5.0,2
+ORD202400798,2024-01-01,家居用品,空气炸锅,338.62,3,914.27,0.1,华南,线上商城,微信支付,4.5,4
+ORD202400799,2024-06-08,食品饮料,红茶礼盒,111.01,3,316.38,0.05,东北,线下门店,银行卡,3.2,4
+ORD202400800,2024-12-16,电子产品,充电宝,125.48,1,112.93,0.1,西南,直播带货,支付宝,4.7,3

Co-creation-projects/minghaoxia61-web-DataAnalyst/main.ipynb

@@ -0,0 +1,745 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "49092bc2",
+   "metadata": {},
+   "source": [
+    "# DataAnalyst - 智能数据分析助手\n",
+    "\n",
+    "> 基于 HelloAgents 框架的三阶段多智能体数据分析流水线：**规划 → 分析 → 报告**，一键把任意 CSV 变成图文并茂的数据分析报告。\n",
+    "\n",
+    "## 项目简介\n",
+    "\n",
+    "数据分析是业务决策的重要环节，但人工分析耗时长、容易遗漏数据中的关键模式。DataAnalyst 让你只需**替换一个 CSV 文件**，即可自动完成：数据探查 → 分析任务规划 → 多工具深度分析 → 自动生成图表 → 撰写 Markdown 分析报告。\n",
+    "\n",
+    "## 架构设计\n",
+    "\n",
+    "```\n",
+    "                 ┌─────────────────────────────────────────────┐\n",
+    "  sales_data.csv │                                             │\n",
+    "  (任意CSV) ───► │  阶段1 规划师Planner(ReActAgent)             │\n",
+    "                 │   └─ 调用 data_overview 探查数据             │\n",
+    "                 │   └─ 输出 3~5 个分析任务(JSON)               │\n",
+    "                 │                                             │\n",
+    "                 │  阶段2 分析员Analyst(ReActAgent)             │\n",
+    "                 │   └─ 逐任务调用6个分析工具(统计/相关性/      │\n",
+    "                 │      异常检测/聚合/绘图)并给出数字结论       │\n",
+    "                 │                                             │\n",
+    "                 │  阶段3 撰写师Reporter(SimpleAgent)           │\n",
+    "                 │   └─ 汇总结论 → Markdown报告 + 嵌入图表      │\n",
+    "                 └─────────────────────────────────────────────┘\n",
+    "                                │\n",
+    "                                ▼\n",
+    "              outputs/analysis_report.md + outputs/charts/*.png\n",
+    "```\n",
+    "\n",
+    "## 作者信息\n",
+    "- 姓名：夏明浩\n",
+    "- GitHub：[@minghaoxia61-web](https://github.com/minghaoxia61-web)\n",
+    "- 日期：2026-09-19"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "192fd6c0",
+   "metadata": {},
+   "source": [
+    "## 第1部分：环境配置"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19abc4a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 导入必要的库\n",
+    "import os\n",
+    "import json\n",
+    "import re\n",
+    "import glob\n",
+    "import warnings\n",
+    "from typing import Any, Dict, List\n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "from hello_agents import HelloAgentsLLM, SimpleAgent, ReActAgent, ToolRegistry, Config\n",
+    "from hello_agents.tools import Tool, ToolParameter, ToolResponse, ToolErrorCode\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "# 加载 .env 中的 LLM 配置（LLM_MODEL_ID / LLM_API_KEY / LLM_BASE_URL，参考 .env.example）\n",
+    "load_dotenv()\n",
+    "\n",
+    "# matplotlib 中文显示设置（Windows 用微软雅黑，macOS/Linux 自动回退）\n",
+    "plt.rcParams[\"font.sans-serif\"] = [\"Microsoft YaHei\", \"SimHei\", \"PingFang SC\", \"Noto Sans CJK SC\", \"sans-serif\"]\n",
+    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
+    "\n",
+    "# 项目路径约定\n",
+    "DATA_PATH = \"data/sales_data.csv\"      # 待分析的数据集：换成你自己的 CSV 即可，无需改任何代码\n",
+    "OUTPUT_DIR = \"outputs\"                 # 分析报告与图表的输出目录\n",
+    "CHART_DIR = os.path.join(OUTPUT_DIR, \"charts\")\n",
+    "os.makedirs(CHART_DIR, exist_ok=True)\n",
+    "\n",
+    "if not os.getenv(\"LLM_API_KEY\"):\n",
+    "    print(\"⚠️ 未检测到 LLM_API_KEY，请先复制 .env.example 为 .env 并填入你的 API 密钥\")\n",
+    "else:\n",
+    "    print(\"✅ 环境配置完成，LLM 模型:\", os.getenv(\"LLM_MODEL_ID\", \"未设置(将使用框架默认值)\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a806291",
+   "metadata": {},
+   "source": [
+    "## 第2部分：数据准备\n",
+    "\n",
+    "本项目自带一份模拟电商销售数据（800 条订单，含季节性、地区差异、渠道差异、少量缺失值与异常大额订单）。\n",
+    "**要分析你自己的数据，只需把 CSV 放到 `data/` 目录并修改上面的 `DATA_PATH`。**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b63e2d11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 读取数据集\n",
+    "df = pd.read_csv(DATA_PATH, encoding=\"utf-8-sig\")\n",
+    "GLOBAL_DF = df   # 工具层共享的数据引用\n",
+    "\n",
+    "print(f\"数据集规模: {df.shape[0]} 行 × {df.shape[1]} 列\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1453ce2e",
+   "metadata": {},
+   "source": [
+    "## 第3部分：数据分析工具定义\n",
+    "\n",
+    "基于 hello-agents 的 `Tool` 基类实现 6 个数据分析工具，每个工具负责一类原子分析能力，返回 `ToolResponse`（LLM 阅读的文本 + 结构化数据）：\n",
+    "\n",
+    "| 工具 | 功能 |\n",
+    "|---|---|\n",
+    "| `data_overview` | 数据概览：行列数、类型、缺失率、唯一值、数值列统计摘要 |\n",
+    "| `column_profile` | 单列画像：数值列统计量 / 类别列频次 Top 榜 |\n",
+    "| `correlation_analysis` | 数值列两两皮尔逊相关系数，按绝对值排序 |\n",
+    "| `group_aggregate` | 按类别列分组聚合（sum/mean/count...），返回 Top N |\n",
+    "| `detect_outliers` | IQR 异常值检测：阈值、数量、最大异常样本 |\n",
+    "| `plot_chart` | 6 种统计图表（直方图/柱状/箱线/折线/散点/热力图），自动处理中文字体并保存 PNG |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "396b0874",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========================================\n",
+    "# 工具1-3：概览 / 列画像 / 相关性\n",
+    "# ========================================\n",
+    "class DataOverviewTool(Tool):\n",
+    "    \"\"\"输出数据集整体概况，供规划智能体了解数据结构\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            name=\"data_overview\",\n",
+    "            description=\"获取数据集整体概况：行列数、每列类型/缺失/唯一值、数值列统计摘要。分析开始前应先调用本工具。\",\n",
+    "        )\n",
+    "\n",
+    "    def get_parameters(self) -> List[ToolParameter]:\n",
+    "        return []\n",
+    "\n",
+    "    def run(self, parameters: Dict[str, Any]) -> ToolResponse:\n",
+    "        df = GLOBAL_DF\n",
+    "        lines = [f\"数据规模: {df.shape[0]} 行 × {df.shape[1]} 列\", \"\", \"字段概况:\"]\n",
+    "        for col in df.columns:\n",
+    "            s = df[col]\n",
+    "            missing = int(s.isna().sum())\n",
+    "            miss_pct = missing / len(df) * 100\n",
+    "            uniq = int(s.nunique(dropna=True))\n",
+    "            line = f\"- {col} | 类型:{s.dtype} | 缺失:{missing}({miss_pct:.1f}%) | 唯一值:{uniq}\"\n",
+    "            if uniq <= 8:\n",
+    "                tops = s.value_counts().head(3)\n",
+    "                line += \" | 高频值: \" + \", \".join(f\"{k}({v})\" for k, v in tops.items())\n",
+    "            lines.append(line)\n",
+    "        num_cols = df.select_dtypes(include=\"number\").columns.tolist()\n",
+    "        if num_cols:\n",
+    "            lines.append(\"\")\n",
+    "            lines.append(\"数值列统计摘要:\")\n",
+    "            lines.append(df[num_cols].describe().T.round(2).to_string())\n",
+    "        return ToolResponse.success(text=\"\\n\".join(lines))\n",
+    "\n",
+    "\n",
+    "class ColumnProfileTool(Tool):\n",
+    "    \"\"\"深入分析单个指定列\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            name=\"column_profile\",\n",
+    "            description=\"深入分析单个指定列：数值列返回均值/标准差/分位数/偏度，类别列返回频次Top榜单，日期列返回时间范围。\",\n",
+    "        )\n",
+    "\n",
+    "    def get_parameters(self) -> List[ToolParameter]:\n",
+    "        return [ToolParameter(name=\"column\", type=\"string\",\n",
+    "                              description=\"要分析的列名（须与数据集列名完全一致）\", required=True)]\n",
+    "\n",
+    "    def run(self, parameters: Dict[str, Any]) -> ToolResponse:\n",
+    "        col = parameters.get(\"column\", \"\")\n",
+    "        df = GLOBAL_DF\n",
+    "        if col not in df.columns:\n",
+    "            return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM,\n",
+    "                                      message=f\"列不存在: {col}。可用列: {list(df.columns)}\")\n",
+    "        s = df[col]\n",
+    "        lines = [f\"列 {col} 画像（非空 {int(s.notna().sum())} / {len(s)}）\"]\n",
+    "        if pd.api.types.is_datetime64_any_dtype(s):\n",
+    "            lines.append(f\"时间范围: {s.min()} ~ {s.max()}\")\n",
+    "        elif pd.api.types.is_numeric_dtype(s):\n",
+    "            lines.append(s.describe().round(2).to_string())\n",
+    "            lines.append(f\"偏度: {s.skew():.2f}\")\n",
+    "        else:\n",
+    "            vc = s.value_counts().head(8)\n",
+    "            lines.append(\"频次Top8:\")\n",
+    "            for k, v in vc.items():\n",
+    "                lines.append(f\"- {k}: {v} ({v / len(s) * 100:.1f}%)\")\n",
+    "        return ToolResponse.success(text=\"\\n\".join(lines))\n",
+    "\n",
+    "\n",
+    "class CorrelationTool(Tool):\n",
+    "    \"\"\"数值列两两相关性分析\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            name=\"correlation_analysis\",\n",
+    "            description=\"计算所有数值列两两之间的皮尔逊相关系数，返回相关性最强的字段对（按绝对值降序）。用于发现字段间的线性关联。\",\n",
+    "        )\n",
+    "\n",
+    "    def get_parameters(self) -> List[ToolParameter]:\n",
+    "        return [ToolParameter(name=\"top_n\", type=\"integer\", description=\"返回相关性最强的前N对字段，默认10\", required=False)]\n",
+    "\n",
+    "    def run(self, parameters: Dict[str, Any]) -> ToolResponse:\n",
+    "        num = GLOBAL_DF.select_dtypes(include=\"number\")\n",
+    "        if num.shape[1] < 2:\n",
+    "            return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM, message=\"数值列不足2列，无法计算相关性\")\n",
+    "        corr = num.corr().round(3)\n",
+    "        pairs = []\n",
+    "        cols = corr.columns\n",
+    "        for i in range(len(cols)):\n",
+    "            for j in range(i + 1, len(cols)):\n",
+    "                pairs.append((cols[i], cols[j], corr.iloc[i, j]))\n",
+    "        pairs.sort(key=lambda x: abs(x[2]), reverse=True)\n",
+    "        top_n = int(parameters.get(\"top_n\") or 10)\n",
+    "        lines = [\"相关性最强的字段对（皮尔逊系数）:\"]\n",
+    "        for a, b, r in pairs[:top_n]:\n",
+    "            strength = \"强\" if abs(r) >= 0.7 else (\"中等\" if abs(r) >= 0.4 else \"弱\")\n",
+    "            lines.append(f\"- {a} × {b}: {r:+.3f}（{strength}{'负' if r < 0 else '正'}相关）\")\n",
+    "        return ToolResponse.success(text=\"\\n\".join(lines), data={\"matrix\": corr.to_dict()})\n",
+    "\n",
+    "print(\"✅ 工具1-3定义完成：data_overview / column_profile / correlation_analysis\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "603fb52e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========================================\n",
+    "# 工具4-6：分组聚合 / 异常检测 / 绘图\n",
+    "# ========================================\n",
+    "class GroupAggregateTool(Tool):\n",
+    "    \"\"\"按类别列分组聚合统计\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            name=\"group_aggregate\",\n",
+    "            description=\"按某个类别列分组，对某个数值列做聚合统计（sum/mean/count/max/min），返回Top N分组结果。适合对比不同类别/地区/渠道的指标。\",\n",
+    "        )\n",
+    "\n",
+    "    def get_parameters(self) -> List[ToolParameter]:\n",
+    "        return [\n",
+    "            ToolParameter(name=\"group_col\", type=\"string\", description=\"分组列名（类别列，如：地区、销售渠道、产品类别）\", required=True),\n",
+    "            ToolParameter(name=\"value_col\", type=\"string\", description=\"被聚合的数值列名（如：销售额）\", required=True),\n",
+    "            ToolParameter(name=\"agg\", type=\"string\", description=\"聚合方式: sum/mean/count/max/min，默认sum\", required=False),\n",
+    "            ToolParameter(name=\"top_n\", type=\"integer\", description=\"返回前N组，默认10\", required=False),\n",
+    "        ]\n",
+    "\n",
+    "    def run(self, parameters: Dict[str, Any]) -> ToolResponse:\n",
+    "        df = GLOBAL_DF\n",
+    "        g, v = parameters.get(\"group_col\", \"\"), parameters.get(\"value_col\", \"\")\n",
+    "        agg = (parameters.get(\"agg\") or \"sum\").lower()\n",
+    "        top_n = int(parameters.get(\"top_n\") or 10)\n",
+    "        if g not in df.columns or v not in df.columns:\n",
+    "            return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM,\n",
+    "                                      message=f\"列不存在。可用列: {list(df.columns)}\")\n",
+    "        if agg not in {\"sum\", \"mean\", \"count\", \"max\", \"min\"}:\n",
+    "            return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM, message=f\"不支持的聚合方式: {agg}\")\n",
+    "        res = df.groupby(g)[v].agg(agg).sort_values(ascending=False)\n",
+    "        share = (res / res.sum() * 100).round(1) if agg == \"sum\" else None\n",
+    "        lines = [f\"按 {g} 分组对 {v} 做 {agg}（Top {min(top_n, len(res))}）:\"]\n",
+    "        for i, (k, val) in enumerate(res.head(top_n).items(), 1):\n",
+    "            s = f\" | 占比 {share[k]}%\" if share is not None else \"\"\n",
+    "            lines.append(f\"{i}. {k}: {round(float(val), 2)}{s}\")\n",
+    "        return ToolResponse.success(text=\"\\n\".join(lines), data={\"result\": res.head(top_n).to_dict()})\n",
+    "\n",
+    "\n",
+    "class OutlierTool(Tool):\n",
+    "    \"\"\"IQR 异常值检测\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            name=\"detect_outliers\",\n",
+    "            description=\"用IQR方法检测指定数值列的异常值：返回正常范围阈值、异常点数量与占比、最大的异常样本。\",\n",
+    "        )\n",
+    "\n",
+    "    def get_parameters(self) -> List[ToolParameter]:\n",
+    "        return [ToolParameter(name=\"column\", type=\"string\", description=\"要检测异常值的数值列名\", required=True)]\n",
+    "\n",
+    "    def run(self, parameters: Dict[str, Any]) -> ToolResponse:\n",
+    "        col = parameters.get(\"column\", \"\")\n",
+    "        df = GLOBAL_DF\n",
+    "        if col not in df.columns:\n",
+    "            return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM,\n",
+    "                                      message=f\"列不存在。可用列: {list(df.columns)}\")\n",
+    "        s = pd.to_numeric(df[col], errors=\"coerce\").dropna()\n",
+    "        q1, q3 = s.quantile(0.25), s.quantile(0.75)\n",
+    "        iqr = q3 - q1\n",
+    "        low, high = q1 - 1.5 * iqr, q3 + 1.5 * iqr\n",
+    "        mask = (s < low) | (s > high)\n",
+    "        out = df.loc[s[mask].index]\n",
+    "        lines = [f\"列 {col} 的IQR异常检测:\",\n",
+    "                 f\"- 正常范围: [{low:.2f}, {high:.2f}]（Q1={q1:.2f}, Q3={q3:.2f}）\",\n",
+    "                 f\"- 异常点: {len(out)} 个，占比 {len(out) / len(df) * 100:.2f}%\"]\n",
+    "        if len(out):\n",
+    "            id_cols = [c for c in df.columns if (\"ID\" in c or \"日期\" in c) and c != col][:2]\n",
+    "            top = out.sort_values(col, ascending=False).head(5)\n",
+    "            lines.append(\"- 最大的异常样本:\")\n",
+    "            lines.append(top[id_cols + [col]].to_string(index=False))\n",
+    "        return ToolResponse.success(text=\"\\n\".join(lines))\n",
+    "\n",
+    "\n",
+    "_CHART_SEQ = {\"n\": 0}   # 图表编号（避免文件名冲突）\n",
+    "\n",
+    "def _safe_name(x: str) -> str:\n",
+    "    \"\"\"把列名转成安全的文件名片段\"\"\"\n",
+    "    return \"\".join(c if c.isalnum() else \"_\" for c in str(x))[:20] or \"chart\"\n",
+    "\n",
+    "\n",
+    "class PlotChartTool(Tool):\n",
+    "    \"\"\"生成统计图表并保存 PNG，返回可嵌入 Markdown 的相对路径\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        super().__init__(\n",
+    "            name=\"plot_chart\",\n",
+    "            description=\"生成统计图表并保存为PNG（中文可正常显示）。类型: histogram/bar/box/line/scatter/heatmap。返回图片相对路径（相对outputs目录），可直接嵌入Markdown报告。\",\n",
+    "        )\n",
+    "\n",
+    "    def get_parameters(self) -> List[ToolParameter]:\n",
+    "        return [\n",
+    "            ToolParameter(name=\"chart_type\", type=\"string\",\n",
+    "                          description=\"图表类型: histogram/bar/box/line/scatter/heatmap\", required=True),\n",
+    "            ToolParameter(name=\"x_column\", type=\"string\", description=\"X轴列名（heatmap可留空）\", required=False),\n",
+    "            ToolParameter(name=\"y_column\", type=\"string\", description=\"Y轴数值列名（histogram/box/heatmap可留空）\", required=False),\n",
+    "            ToolParameter(name=\"agg\", type=\"string\", description=\"bar/line图的聚合方式: sum/mean/count，默认sum\", required=False),\n",
+    "            ToolParameter(name=\"title\", type=\"string\", description=\"图表标题（中文），默认自动生成\", required=False),\n",
+    "        ]\n",
+    "\n",
+    "    def run(self, parameters: Dict[str, Any]) -> ToolResponse:\n",
+    "        df = GLOBAL_DF\n",
+    "        ctype = (parameters.get(\"chart_type\") or \"\").lower()\n",
+    "        x, y = parameters.get(\"x_column\") or \"\", parameters.get(\"y_column\") or \"\"\n",
+    "        agg = (parameters.get(\"agg\") or \"sum\").lower()\n",
+    "        title = parameters.get(\"title\") or \"\"\n",
+    "        num_cols = df.select_dtypes(include=\"number\").columns.tolist()\n",
+    "\n",
+    "        if ctype not in {\"histogram\", \"bar\", \"box\", \"line\", \"scatter\", \"heatmap\"}:\n",
+    "            return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM, message=f\"不支持的图表类型: {ctype}\")\n",
+    "        if ctype != \"heatmap\":   # 校验列名\n",
+    "            need = {\"histogram\": [], \"box\": [x or y], \"bar\": [x, y], \"line\": [x, y], \"scatter\": [x, y]}[ctype]\n",
+    "            for c in [c for c in need if c]:\n",
+    "                if c not in df.columns:\n",
+    "                    return ToolResponse.error(code=ToolErrorCode.INVALID_PARAM,\n",
+    "                                              message=f\"列不存在: {c}。可用列: {list(df.columns)}\")\n",
+    "        if agg not in {\"sum\", \"mean\", \"count\"}:\n",
+    "            agg = \"sum\"\n",
+    "\n",
+    "        fig, ax = plt.subplots(figsize=(8, 5))\n",
+    "        if ctype == \"heatmap\":\n",
+    "            corr = df[num_cols].corr()\n",
+    "            im = ax.imshow(corr, cmap=\"coolwarm\", vmin=-1, vmax=1)\n",
+    "            ax.set_xticks(range(len(corr.columns)), corr.columns, rotation=45, ha=\"right\")\n",
+    "            ax.set_yticks(range(len(corr.columns)), corr.columns)\n",
+    "            for i in range(len(corr.columns)):\n",
+    "                for j in range(len(corr.columns)):\n",
+    "                    ax.text(j, i, f\"{corr.iloc[i, j]:.2f}\", ha=\"center\", va=\"center\", fontsize=8)\n",
+    "            fig.colorbar(im, ax=ax, shrink=0.8)\n",
+    "            title = title or \"数值列相关性热力图\"\n",
+    "        elif ctype == \"histogram\":\n",
+    "            if not num_cols:\n",
+    "                return ToolResponse.error(code=ToolErrorCode.EXECUTION_ERROR, message=\"数据集中没有数值列，无法绘制直方图\")\n",
+    "            col = y or x or num_cols[0]\n",
+    "            data = pd.to_numeric(df[col], errors=\"coerce\").dropna()\n",
+    "            if data.empty:\n",
+    "                return ToolResponse.error(code=ToolErrorCode.EXECUTION_ERROR,\n",
+    "                                          message=f\"列 {col} 无法转换为数值，无法绘制直方图\")\n",
+    "            ax.hist(data, bins=30, color=\"#4C72B0\", edgecolor=\"white\")\n",
+    "            ax.set_xlabel(col)\n",
+    "            ax.set_ylabel(\"频数\")\n",
+    "            title = title or f\"{col} 的分布直方图\"\n",
+    "        elif ctype == \"box\":\n",
+    "            col = y or x\n",
+    "            data = pd.to_numeric(df[col], errors=\"coerce\").dropna()\n",
+    "            if data.empty:\n",
+    "                return ToolResponse.error(code=ToolErrorCode.EXECUTION_ERROR,\n",
+    "                                          message=f\"列 {col} 无法绘制箱线图（非数值列或无有效数据）\")\n",
+    "            ax.boxplot(data, vert=True, tick_labels=[col])\n",
+    "            ax.set_ylabel(col)\n",
+    "            title = title or f\"{col} 的箱线图\"\n",
+    "        elif ctype == \"bar\":\n",
+    "            tmp = df.dropna(subset=[x])\n",
+    "            if agg == \"count\" or not y:\n",
+    "                res = tmp.groupby(x).size().sort_values(ascending=False)\n",
+    "                ylab = \"数量\"\n",
+    "            else:\n",
+    "                res = tmp.groupby(x)[y].agg(agg).sort_values(ascending=False)\n",
+    "                ylab = f\"{y}（{agg}）\"\n",
+    "            res = res.head(10)\n",
+    "            ax.barh([str(k) for k in res.index][::-1], list(res.values)[::-1], color=\"#4C72B0\")\n",
+    "            ax.set_xlabel(ylab)\n",
+    "            title = title or f\"各{x}的{ylab}对比（Top{len(res)}）\"\n",
+    "        elif ctype == \"line\":\n",
+    "            tmp = df.dropna(subset=[x, y]).copy()\n",
+    "            xt = pd.to_datetime(tmp[x], errors=\"coerce\")\n",
+    "            if xt.notna().sum() > len(tmp) * 0.8:      # 日期列：按月重采样\n",
+    "                tmp[\"_t\"] = xt\n",
+    "                res = tmp.set_index(\"_t\")[y].resample(\"ME\").agg(agg)\n",
+    "                ax.plot(res.index, res.values, marker=\"o\", color=\"#4C72B0\")\n",
+    "                ax.set_xlabel(x + \"（按月）\")\n",
+    "            else:\n",
+    "                res = tmp.groupby(x)[y].agg(agg).sort_index()\n",
+    "                ax.plot([str(k) for k in res.index], res.values, marker=\"o\", color=\"#4C72B0\")\n",
+    "                ax.set_xlabel(x)\n",
+    "            ax.set_ylabel(f\"{y}（{agg}）\")\n",
+    "            title = title or f\"{y}随{x}的变化趋势（{agg}）\"\n",
+    "        else:                                          # scatter\n",
+    "            tmp = df.dropna(subset=[x, y])\n",
+    "            if tmp.empty:\n",
+    "                return ToolResponse.error(code=ToolErrorCode.EXECUTION_ERROR,\n",
+    "                                          message=f\"列 {x} 或 {y} 无有效成对数据，无法绘制散点图\")\n",
+    "            if len(tmp) > 2000:\n",
+    "                tmp = tmp.sample(2000, random_state=42)\n",
+    "            ax.scatter(tmp[x], tmp[y], s=12, alpha=0.5, color=\"#4C72B0\")\n",
+    "            ax.set_xlabel(x)\n",
+    "            ax.set_ylabel(y)\n",
+    "            title = title or f\"{x} 与 {y} 的散点图\"\n",
+    "        ax.set_title(title)\n",
+    "        ax.grid(alpha=0.25)\n",
+    "\n",
+    "        _CHART_SEQ[\"n\"] += 1\n",
+    "        # 文件名使用实际绘制的列（箱线图/直方图取被统计列，热力图为corr）\n",
+    "        name_col = {\"heatmap\": \"corr\", \"histogram\": (y or x or (num_cols[0] if num_cols else \"data\")),\n",
+    "                    \"box\": (y or x), \"bar\": x, \"line\": x, \"scatter\": x}[ctype]\n",
+    "        fname = f\"chart_{_CHART_SEQ['n']}_{ctype}_{_safe_name(name_col)}.png\"\n",
+    "        rel = f\"charts/{fname}\"\n",
+    "        fig.savefig(os.path.join(CHART_DIR, fname), dpi=150, bbox_inches=\"tight\")\n",
+    "        plt.close(\"all\")\n",
+    "        return ToolResponse.success(\n",
+    "            text=(f\"图表已生成: {rel}（相对 {OUTPUT_DIR}/ 目录）\\n标题: {title}\\n\"\n",
+    "                  f\"请在报告对应小节用 ![图表描述]({rel}) 嵌入该图片。\"),\n",
+    "            data={\"path\": rel},\n",
+    "        )\n",
+    "\n",
+    "print(\"✅ 工具4-6定义完成：group_aggregate / detect_outliers / plot_chart\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39ac9f19",
+   "metadata": {},
+   "source": [
+    "## 第4部分：智能体构建\n",
+    "\n",
+    "| 智能体 | 范式 | 职责 | 配备工具 |\n",
+    "|---|---|---|---|\n",
+    "| 分析规划师 Planner | ReActAgent | 探查数据、规划 3~5 个分析任务（JSON） | data_overview、column_profile |\n",
+    "| 数据分析员 Analyst | ReActAgent | 逐任务调用工具完成分析并给出数字结论 | 全部 6 个工具 |\n",
+    "| 报告撰写师 Reporter | SimpleAgent | 汇总结论，撰写图文并茂的 Markdown 报告 | 无（纯生成） |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d87179c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========================================\n",
+    "# 工具注册表：规划智能体配轻量探查工具，分析智能体配全部分析工具\n",
+    "# ========================================\n",
+    "planner_registry = ToolRegistry()\n",
+    "planner_registry.register_tool(DataOverviewTool())\n",
+    "planner_registry.register_tool(ColumnProfileTool())\n",
+    "\n",
+    "analysis_registry = ToolRegistry()\n",
+    "for t in [DataOverviewTool(), ColumnProfileTool(), CorrelationTool(),\n",
+    "          GroupAggregateTool(), OutlierTool(), PlotChartTool()]:\n",
+    "    analysis_registry.register_tool(t)\n",
+    "\n",
+    "# LLM 客户端（自动读取 .env 中的 LLM_MODEL_ID / LLM_API_KEY / LLM_BASE_URL）\n",
+    "llm = HelloAgentsLLM()\n",
+    "\n",
+    "# 说明: hello-agents 1.0.0 的 TraceLogger 在每次 run() 结束时关闭轨迹文件，\n",
+    "# 同一智能体第二次 run() 时会因写入已关闭的文件句柄而报错（I/O operation on closed file）。\n",
+    "# 流水线需要对同一智能体多轮调用，因此这里关闭轨迹追踪（不影响分析与报告结果）。\n",
+    "agent_config = Config(trace_enabled=False)\n",
+    "\n",
+    "PLANNER_PROMPT = \"\"\"你是一位资深数据分析规划师，负责为已加载到内存中的CSV数据集制定分析计划。\n",
+    "\n",
+    "你的工作流程：\n",
+    "1. 先调用 data_overview 工具了解数据集的字段结构与数据质量（如有需要可再用 column_profile 查看关键列）\n",
+    "2. 结合字段实际含义，规划 3~5 个有业务价值的分析任务，可覆盖：时间趋势、类别结构、分组对比、相关性分析、异常值检测等维度\n",
+    "3. 每个任务都要具体可执行：写明使用哪个字段、做什么统计、需要什么图表（图表类型支持: histogram/bar/box/line/scatter/heatmap）\n",
+    "\n",
+    "最后，你必须以纯JSON数组作为最终答案输出分析计划（直接输出JSON本身，不要加代码块围栏，不要输出任何解释文字），格式如下:\n",
+    "[\n",
+    "  {\"task\": \"任务简短标题\", \"goal\": \"具体分析目标，写明字段、统计方式与所需图表\"}\n",
+    "]\"\"\"\n",
+    "\n",
+    "ANALYST_PROMPT = \"\"\"你是一位严谨的数据分析员。针对交给你的每个分析任务：\n",
+    "\n",
+    "1. 选择合适的工具完成分析（group_aggregate / correlation_analysis / detect_outliers / column_profile / plot_chart）\n",
+    "2. 需要展示分布、对比或趋势时，请调用 plot_chart 生成图表（标题用中文）\n",
+    "3. 最终用 3~6 句话总结结论，结论中必须引用工具返回的具体数字，不要空泛\n",
+    "4. 如果生成了图表，在结论最后单独一行列出图片路径，格式: 图表: charts/xxx.png\n",
+    "\n",
+    "注意：一个任务通常 1~3 次工具调用即可完成，不要重复调用完全相同的工具。\"\"\"\n",
+    "\n",
+    "REPORTER_PROMPT = \"\"\"你是一位资深数据分析师，负责把分析结论整理成一份专业的中文数据分析报告（Markdown格式）。\n",
+    "\n",
+    "报告结构要求：\n",
+    "# 报告标题\n",
+    "## 一、数据概况\n",
+    "## 二、核心发现（每个分析任务一个小节，标题概括发现，正文给出结论与关键数字）\n",
+    "## 三、业务建议（基于发现给出 3~5 条可落地的建议）\n",
+    "## 四、分析方法说明（简述使用的工具与统计方法）\n",
+    "\n",
+    "撰写要求：\n",
+    "1. 所有结论与数字必须来自输入内容，禁止编造数据\n",
+    "2. 若某条结论中带有\"图表: charts/xxx.png\"路径，请在对应小节用Markdown图片语法嵌入: ![图表描述](charts/xxx.png)\n",
+    "3. 语言专业、简洁，突出业务洞察\"\"\"\n",
+    "\n",
+    "planner_agent = ReActAgent(name=\"分析规划师\", llm=llm, tool_registry=planner_registry,\n",
+    "                           system_prompt=PLANNER_PROMPT, config=agent_config, max_steps=5)\n",
+    "analyst_agent = ReActAgent(name=\"数据分析员\", llm=llm, tool_registry=analysis_registry,\n",
+    "                           system_prompt=ANALYST_PROMPT, config=agent_config, max_steps=6)\n",
+    "reporter_agent = SimpleAgent(name=\"报告撰写师\", llm=llm, system_prompt=REPORTER_PROMPT,\n",
+    "                             config=agent_config)\n",
+    "\n",
+    "print(\"✅ 三个智能体构建完成：分析规划师(ReAct) / 数据分析员(ReAct) / 报告撰写师(Simple)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1341d72",
+   "metadata": {},
+   "source": [
+    "## 第5部分：三阶段分析流水线\n",
+    "\n",
+    "`run_pipeline()` 串起三个智能体：**规划 → 逐任务分析 → 汇总报告**，并把报告与图表落盘到 `outputs/` 目录。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc18ad8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ========================================\n",
+    "# 流水线实现\n",
+    "# ========================================\n",
+    "def parse_tasks(plan_text: str) -> List[Dict[str, str]]:\n",
+    "    \"\"\"从规划智能体的输出中解析任务列表（JSON优先，正则兜底）\"\"\"\n",
+    "    candidates = []\n",
+    "    m = re.search(r\"```(?:json)?\\s*(\\[.*?\\])\\s*```\", plan_text, re.S)\n",
+    "    if m:\n",
+    "        candidates.append(m.group(1))\n",
+    "    # 兜底1: 输出是裸JSON数组（没有代码块围栏）\n",
+    "    if not candidates:\n",
+    "        i, j = plan_text.find(\"[\"), plan_text.rfind(\"]\")\n",
+    "        if 0 <= i < j:\n",
+    "            candidates.append(plan_text[i:j + 1])\n",
+    "    for cand in candidates:\n",
+    "        try:\n",
+    "            tasks = json.loads(cand)\n",
+    "            return [\n",
+    "                {\"task\": str(t.get(\"task\", \"\")).strip() or f\"任务{i}\",\n",
+    "                 \"goal\": str(t.get(\"goal\", \"\")).strip()}\n",
+    "                for i, t in enumerate(tasks, 1) if isinstance(t, dict)\n",
+    "            ]\n",
+    "        except json.JSONDecodeError:\n",
+    "            continue\n",
+    "    # 兜底2: 按\"任务N：描述\"格式的行提取\n",
+    "    tasks = []\n",
+    "    for line in plan_text.splitlines():\n",
+    "        m2 = re.match(r\"\\s*[-*\\d.、)]*\\s*(?:\\*\\*)?任务\\d+[**:：]?\\s*(.+)\", line)\n",
+    "        if m2 and len(m2.group(1).strip()) > 4:\n",
+    "            tasks.append({\"task\": m2.group(1).strip(), \"goal\": \"\"})\n",
+    "    return tasks[:6]\n",
+    "\n",
+    "\n",
+    "def run_pipeline(data_path: str = DATA_PATH,\n",
+    "                 report_path: str = os.path.join(OUTPUT_DIR, \"analysis_report.md\")):\n",
+    "    \"\"\"三阶段分析流水线：规划 → 逐任务分析 → 汇总报告\"\"\"\n",
+    "    print(\"=\" * 60)\n",
+    "    print(\"【阶段1/3】分析规划：探查数据并生成分析任务\")\n",
+    "    plan_text = planner_agent.run(\n",
+    "        f\"请针对数据集 {data_path}（{GLOBAL_DF.shape[0]} 行 × {GLOBAL_DF.shape[1]} 列，字段: \"\n",
+    "        f\"{', '.join(GLOBAL_DF.columns)}）制定分析计划。\"\n",
+    "    )\n",
+    "    tasks = parse_tasks(plan_text)\n",
+    "    if not tasks:\n",
+    "        tasks = [{\"task\": \"数据整体概览与质量检查\", \"goal\": \"使用 data_overview 了解数据\"}]\n",
+    "    print(f\"\\n✅ 规划完成，共 {len(tasks)} 个分析任务:\")\n",
+    "    for i, t in enumerate(tasks, 1):\n",
+    "        print(f\"   任务{i}: {t['task']}\")\n",
+    "\n",
+    "    print(\"\\n\" + \"=\" * 60)\n",
+    "    print(\"【阶段2/3】逐任务深度分析\")\n",
+    "    conclusions = []\n",
+    "    for i, t in enumerate(tasks, 1):\n",
+    "        print(f\"\\n>>> 执行任务{i}: {t['task']}\")\n",
+    "        out = analyst_agent.run(f\"分析任务: {t['task']}\\n分析目标: {t['goal'] or t['task']}\")\n",
+    "        conclusions.append({\"task\": t[\"task\"], \"conclusion\": out})\n",
+    "        print(f\"✅ 任务{i} 完成\")\n",
+    "\n",
+    "    print(\"\\n\" + \"=\" * 60)\n",
+    "    print(\"【阶段3/3】撰写分析报告\")\n",
+    "    chart_files = sorted(glob.glob(os.path.join(CHART_DIR, \"*.png\")))\n",
+    "    chart_paths = [os.path.relpath(p, OUTPUT_DIR).replace(\"\\\\\\\\\", \"/\") for p in chart_files]\n",
+    "    report_input = (\n",
+    "        f\"数据集概况: {GLOBAL_DF.shape[0]} 行 × {GLOBAL_DF.shape[1]} 列，字段: {', '.join(GLOBAL_DF.columns)}\\n\\n\"\n",
+    "        f\"已生成的图表文件（相对outputs目录）: {json.dumps(chart_paths, ensure_ascii=False)}\\n\\n\"\n",
+    "        f\"各分析任务的结论（JSON）:\\n{json.dumps(conclusions, ensure_ascii=False, indent=2)}\\n\\n\"\n",
+    "        f\"请撰写完整的数据分析报告。\"\n",
+    "    )\n",
+    "    report = reporter_agent.run(report_input)\n",
+    "    with open(report_path, \"w\", encoding=\"utf-8\") as f:\n",
+    "        f.write(report)\n",
+    "    print(f\"\\n✅ 报告已保存: {report_path}\")\n",
+    "    print(f\"✅ 共生成图表 {len(chart_paths)} 张，保存于 {CHART_DIR}/\")\n",
+    "    return report, tasks, conclusions\n",
+    "\n",
+    "\n",
+    "print(\"✅ 流水线函数定义完成\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66fc3483",
+   "metadata": {},
+   "source": [
+    "## 第6部分：运行完整分析\n",
+    "\n",
+    "执行三阶段流水线（需要 `.env` 中配置好 LLM API 密钥）。运行结束后：\n",
+    "- 报告：`outputs/analysis_report.md`\n",
+    "- 图表：`outputs/charts/*.png`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5699e62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "report, tasks, conclusions = run_pipeline()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01a3593a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看生成的分析报告\n",
+    "print(report)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fc78b04",
+   "metadata": {},
+   "source": [
+    "## 第7部分：工具自检（不消耗 LLM 调用）\n",
+    "\n",
+    "直接调用两个核心工具验证工具层工作正常——即使没有配置 API 密钥，也可以运行本单元格体验工具层。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0229e7ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 自检1：分组聚合 —— 各产品类别的销售额贡献\n",
+    "print(GroupAggregateTool().run({\"group_col\": \"产品类别\", \"value_col\": \"销售额\"}).text)\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 50 + \"\\n\")\n",
+    "\n",
+    "# 自检2：IQR异常值检测 —— 找出异常大额订单\n",
+    "print(OutlierTool().run({\"column\": \"销售额\"}).text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2563b7c",
+   "metadata": {},
+   "source": [
+    "## 第8部分：总结与展望\n",
+    "\n",
+    "### 实现的功能\n",
+    "- ✅ 通用 CSV 数据分析：替换数据文件即可分析新数据集，无需改代码\n",
+    "- ✅ 三阶段多智能体流水线：规划（Plan）→ 分析（Execute）→ 报告（Report）\n",
+    "- ✅ 6 个原子分析工具：概览 / 列画像 / 相关性 / 分组聚合 / 异常检测 / 绘图\n",
+    "- ✅ 自动中文图表生成，并在报告中以相对路径嵌入\n",
+    "- ✅ 工具层可独立运行（第7部分自检），便于调试与评审\n",
+    "\n",
+    "### 遇到的挑战与解决方案\n",
+    "- **规划输出不稳定**：LLM 偶尔不按 JSON 输出 → 采用「JSON 优先 + 正则兜底」双解析策略（`parse_tasks`）\n",
+    "- **matplotlib 中文乱码**：统一配置 `font.sans-serif` 候选字体链，并处理负号显示\n",
+    "- **LLM 传参错误**：所有工具对列名/参数做校验并返回可用列提示，智能体可自行纠错重试\n",
+    "\n",
+    "### 未来改进方向\n",
+    "- [ ] 支持多 Sheet / Excel 与数据库数据源\n",
+    "- [ ] 引入 ReflectionAgent 对报告质量自动复审\n",
+    "- [ ] 增加 Gradio 交互界面，支持拖拽上传\n",
+    "- [ ] 分析结果缓存，避免重复计算"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Co-creation-projects/minghaoxia61-web-DataAnalyst/outputs/analysis_report.md

@@ -0,0 +1,119 @@
+# 销售数据分析报告
+
+## 一、数据概况
+
+本报告基于一份包含 **800 行 × 13 列** 的订单级销售数据集。数据字段覆盖交易、产品、渠道与体验四大维度，具体包括：订单ID、订单日期、产品类别、产品名称、单价、数量、销售额、折扣率、地区、销售渠道、支付方式、客户满意度、配送天数。
+
+数据时间跨度覆盖 2024 年全年，包含电子产品、家居用品、服装鞋帽、美妆个护、食品饮料五大产品类别，覆盖华东、华北、华南、西南、东北、华中、西北等地区，销售渠道涵盖线上商城、直播带货、线下门店，支付方式包括微信支付、支付宝、银行卡、货到付款。
+
+分析围绕六个任务展开：销售额时间趋势、产品类别结构、地区与渠道对比、折扣率与满意度及配送关联、销售额异常值检测、支付方式与满意度对比。全年总销售额约 **954,243 元**。
+
+---
+
+## 二、核心发现
+
+### 1. 销售额时间趋势：7月大额订单拉高全年，剔除后呈"下半年走强"季节性
+
+将 800 条订单按月汇总后，销售呈现明显的**非均衡分布**：
+
+| 月份 | 销售额(元) | 订单量(单) |
+|------|-----------|-----------|
+| 1月 | 52,819 | 57 |
+| 2月 | 86,614 | 68 |
+| 3月 | 54,178 | 60 |
+| 4月 | 28,022 | 50 |
+| 5月 | 54,042 | 65 |
+| 6月 | 24,321 | 66 |
+| **7月** | **314,951** | 72 |
+| 8月 | 36,498 | 80 |
+| 9月 | 89,018 | 74 |
+| 10月 | 102,155 | 72 |
+| 11月 | 68,625 | 71 |
+| 12月 | 43,000 | 66 |
+
+**关键结论：**
+
+- **全年销售高度集中于7月**：7月销售额 314,951 元，占全年总额的约 **33%**，但订单量仅 72 单，属"少数大额订单拉高总额"。下钻发现 2024-07-13 单日销售额即达 272,336.85 元（占全年 28.5%），为应剔除的异常值。
+- **剔除异常后呈"下半年走强"季节性**：8–11月销售额（36,498 / 89,018 / 102,155 / 68,625 元）明显高于上半年多数月份，10月为剔除异常后的真实峰值。
+- **订单量走势平稳**：月订单量在 50–80 单区间波动，8月最高（80单）、4月最低（50单），说明销售额剧烈波动并非来自订单数量，而是个别大额订单。
+- **季节性形态**：呈"年中冲高（7月大单）、年末翘尾（10–11月）"双峰，4月、6月为全年低谷。
+
+![2024年月度销售额趋势](charts/chart_1_line_订单日期.png)
+![2024年订单量日度趋势](charts/chart_2_line_订单日期.png)
+
+### 2. 产品类别结构：电子产品"一枝独秀"，呈现高价低频模式
+
+- **电子产品是绝对核心品类**：销售额合计 721,612.94 元，占全部销售额的 **75.6%**，远超其他四类。第二名家居用品仅 88,836.64 元（9.3%），服装鞋帽 83,883.39 元（8.8%），美妆个护 36,100.70 元（3.8%），食品饮料 23,920.44 元（2.5%）。
+- **订单量与销售额严重背离**：服装鞋帽订单量最多（199单）、家居用品 173 单、食品饮料 169 单，而销售额最高的电子产品订单量仅 130 单（倒数第二），说明电子产品依靠高单价驱动，而非订单数量。
+- **客单价差异悬殊**：电子产品平均客单价高达 5,550.87 元，是第二名家居用品（513.51 元）的 **10.8 倍**；服装鞋帽 421.52 元、美妆个护 279.85 元、食品饮料仅 141.54 元。
+
+![产品类别销售额分布](charts/chart_3_bar_产品类别.png)
+![产品类别订单量分布](charts/chart_4_bar_产品类别.png)
+
+### 3. 地区与渠道：华东"一超多强"，线上商城为绝对主力
+
+- **地区维度呈"一超多强"格局**：华东销售额 556,801.25 元，占全国 **58.3%**，远超华北（127,687.37 元，13.4%）和西南（91,531.25 元，9.6%）；华南 77,231.29 元（8.1%），西北最低仅 4,164.94 元（0.4%）。
+- **渠道维度线上化程度极高**：线上商城销售额 656,983.21 元（**68.8%**），直播带货 204,586.32 元（21.4%），线下门店仅 92,784.58 元（9.7%）。
+- **地区内部质量差异明显**：华东客单价最高（2,054.62 元），东北次之（1,478.92 元），华南最低（576.35 元）、西北仅 297.5 元。
+- **优势组合**："华东 × 线上商城"为最强增长引擎；华南、西北等低贡献地区应优先借力线上渠道突破。
+
+![地区销售额分布](charts/chart_5_bar_地区.png)
+![销售渠道销售额分布](charts/chart_6_bar_销售渠道.png)
+
+### 4. 折扣与体验关联：配送时效是满意度关键，折扣几乎无效
+
+- **配送天数与满意度呈显著中等负相关（r = -0.593）**，是所有字段对中最强关系。配送天数均值 2.85 天（范围 1~6 天），配送越慢满意度越低，履约时效是满意度主要驱动因素。
+- **折扣率与销售额几乎无相关（r = -0.036）**，散点图呈杂乱无趋势分布。折扣率均值仅 6%（最高 20%），0 折扣订单最多（317 笔），当前折扣对销售额拉动非常有限。
+- **折扣率与满意度同样微弱负相关（r = -0.103）**，打折并未提升满意度，暗示满意度更多由配送体验决定。
+- **销售额主要由单价（r = +0.333）和数量（r = +0.301）驱动**，符合"高价多量带来高销售额"的逻辑，但解释力有限。
+- 配送天数与销售额（r = -0.068）、折扣率与配送天数（r = +0.056）均近乎无关，说明配送与折扣是两个独立运营维度。
+
+![相关性热力图](charts/chart_7_heatmap_corr.png)
+![折扣率与销售额散点图](charts/chart_8_scatter_折扣率.png)
+![配送天数与满意度散点图](charts/chart_9_scatter_配送天数.png)
+
+### 5. 销售额异常值：约13%订单为离群，高度集中于电子产品与华东
+
+- **离群规模**：IQR 方法显示销售额正常区间为 [-523.04, 1143.28]（Q1=101.83, Q3=518.41），共检出 **103 个异常订单，占比 12.88%**；数量列正常区间 [-0.50, 3.50]，检出 **96 个异常，占比 12.00%**，两列异常比例接近，说明极端大额订单往往同时伴随异常数量。
+- **极端样本**：最大离群订单 ORD202400166（2024-07-13，销售额 **272,198.00**，数量 50），远超第二名 ORD202400719（29,541.96）近 9 倍；数量列最大异常为 ORD202400729（数量 **200**，为正常上限的 57 倍），属明显录入或团购批量订单。
+- **分布特征**：销售额均值 1192.94 远高于中位数 226.96，标准差高达 9916.53，最大值 272,198 是中位数的 1200 倍，偏度达 25.72，呈严重右偏。
+- **离群分布**：异常高度集中于**电子产品**（最大 272,198，其他类别最高仅 3,712）、**线上商城**渠道、**华东**地区。
+
+![销售额箱线图](charts/chart_10_box_销售额.png)
+![销售额分布直方图](charts/chart_11_histogram_销售额.png)
+
+### 6. 支付方式与满意度：差异极小，非关键影响因素
+
+- **满意度差异极小**：银行卡与支付宝并列最高（均 4.48），微信支付 4.47，货到付款最低（4.41），极差仅 **0.07 分**。
+- **配送天数呈相反趋势**：微信支付与支付宝并列最长（均 2.88 天），满意度最低的货到付款配送反而最快（2.76 天），银行卡最快（2.69 天）。
+- **样本高度集中**：微信支付 370 单、支付宝 274 单，而货到付款与银行卡各仅 78 单，小样本可能放大后两者波动。
+- **结论**：不同支付方式的服务体验高度趋同，满意度差异（<0.1 分）与配送差异（<0.2 天）均不显著，支付方式并非影响满意度的关键因素。
+
+![支付方式对比](charts/chart_12_bar_支付方式.png)
+
+---
+
+## 三、业务建议
+
+1. **建立大额订单监控与归因机制**：全年约 13% 订单为离群值，且 7月单笔 27 万元订单直接扭曲月度趋势（占比 28.5%）。建议对 ORD202400166、ORD202400729 等极端订单单独核验（核查是否为批发/团购或录入错误），并在日常报表中采用**中位数或对数变换**呈现销售额，避免异常值误导决策。
+
+2. **优先优化配送时效以提升满意度**：配送天数与满意度相关性最强（r = -0.593），而折扣对满意度和销售额均几乎无正向作用。建议将资源从"折扣促销"转向"履约提速"，重点压缩配送天数（当前均值 2.85 天，目标向 2 天以内靠拢），尤其针对配送较慢的微信支付与支付宝订单。
+
+3. **巩固"华东 × 线上商城"核心基本盘，同时破解单一依赖**：华东贡献 58.3% 销售额、线上商城贡献 68.8%，两者叠加是最强引擎，应优先保障库存与履约资源。但电子产品单品类占比高达 75.6%，存在结构性风险，需防范品类波动对整体业绩的冲击。
+
+4. **对"高频低客单"品类实施客单价提升策略**：家居用品、服装鞋帽、食品饮料订单量高但客单价低（141–513 元），建议通过组合销售、满减凑单、关联推荐等方式提升客单价，平衡对电子产品的过度依赖。
+
+5. **针对低贡献地区精准投放**：华南（客单价仅 576.35 元）、西北（297.5 元）等地区贡献低且客单价低，应优先借力线上商城/直播带货的高效渠道（而非占比仅 9.7% 的线下门店）实现突破，并针对华南等高订单量地区优化客单价策略。
+
+---
+
+## 四、分析方法说明
+
+- **工具与流程**：基于 Python 数据分析栈（pandas 进行数据清洗与分组聚合，matplotlib/seaborn 完成可视化），按六个分析任务分别产出结论与图表。
+- **时间趋势分析**：将订单日期解析为月份后进行月度汇总（销售额、订单量），并对异常月份进行单日下钻定位。
+- **结构对比分析**：按产品类别、地区、销售渠道、支付方式进行分组聚合，比较销售额、订单量与客单价（平均销售额）。
+- **相关性分析**：采用 Pearson 相关系数矩阵（热力图）量化折扣率、配送天数、单价、数量、销售额、满意度之间的线性关系，并结合散点图判断趋势形态。
+- **异常值检测**：采用 IQR（四分位距）方法，以 [Q1-1.5×IQR, Q3+1.5×IQR] 为正常区间识别销售额与数量的离群订单，并统计其类别、渠道、地区分布；结合均值、中位数、标准差、偏度刻画分布形态。
+- **分组对比**：对支付方式分组的满意度与配送天数进行均值对比，评估组间差异显著性（极差）。
+
+> 说明：本报告所有结论与数字均来自输入的分析结论，未作额外推断或编造。

Co-creation-projects/minghaoxia61-web-DataAnalyst/requirements.txt

@@ -0,0 +1,12 @@
+# 核心框架
+hello-agents[all]>=0.2.7
+
+# 数据分析与可视化
+pandas>=2.0.0
+matplotlib>=3.7.0
+
+# 环境变量加载
+python-dotenv>=1.0.0
+
+# Notebook 运行环境（可选，也可以用 pip install jupyterlab）
+jupyterlab>=4.0.0