{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DataAnalysisAgent - 智能数据分析助手"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第1部分：环境配置"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ 库导入和配置完成\n"
]
}
],
"source": [
"# 导入库和配置\n",
"from hello_agents import SimpleAgent, HelloAgentsLLM\n",
"from hello_agents.tools import Tool, ToolParameter\n",
"from typing import Dict, Any, List\n",
"import ast\n",
"import os\n",
"\n",
"# 配置LLM参数\n",
"os.environ[\"LLM_MODEL_ID\"] = \"Qwen/Qwen3-8B\"\n",
"os.environ[\"LLM_API_KEY\"] = \"ms-9382e20f-96c2-456a-b609-af5c81201066\"\n",
"os.environ[\"LLM_BASE_URL\"] = \"https://api-inference.modelscope.cn/v1/\"\n",
"os.environ[\"LLM_TIMEOUT\"] = \"60\"\n",
"\n",
"print(\"✅ 库导入和配置完成\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第2部分：定义数据分析工具"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ DataCleaningTool 定义完成\n"
]
}
],
"source": [
"import json\n",
"import pandas as pd\n",
"from typing import Dict, Any, List\n",
"\n",
"class DataCleaningTool(Tool):\n",
" \"\"\"数据清洗工具 - 基于用户指定规则清洗表格数据\"\"\"\n",
"\n",
" def __init__(self):\n",
" super().__init__(\n",
" name=\"data_cleaner\",\n",
" description=\"对传入的表格数据执行清洗操作，包括去空值、列筛选等\"\n",
" )\n",
"\n",
" def run(self, parameters: Dict[str, Any]) -> str:\n",
" \"\"\"\n",
" 执行数据清洗\n",
" parameters 应包含：\n",
" - data_json: str，来自 excel_reader 的 JSON 字符串（必须）\n",
" - drop_na: bool，是否删除含空值的行（默认 False）\n",
" - columns_to_keep: List[str]，保留的列名列表（可选）\n",
" \"\"\"\n",
" data_json = parameters.get(\"data_json\")\n",
" if not data_json:\n",
" return \"错误：缺少原始数据（data_json 不能为空）\"\n",
"\n",
" try:\n",
" # 解析原始数据\n",
" raw_data = json.loads(data_json)\n",
" records = raw_data.get(\"完整数据\", [])\n",
" if not records:\n",
" return \"警告：原始数据为空，无法清洗\"\n",
"\n",
" df = pd.DataFrame(records)\n",
"\n",
" # 1. 列筛选\n",
" columns_to_keep = parameters.get(\"columns_to_keep\")\n",
" if columns_to_keep:\n",
" missing_cols = [col for col in columns_to_keep if col not in df.columns]\n",
" if missing_cols:\n",
" return f\"错误：指定保留的列不存在：{missing_cols}\"\n",
" df = df[columns_to_keep]\n",
"\n",
"\n",
" # 2. 删除空值行\n",
" if parameters.get(\"drop_na\", False):\n",
" original_len = len(df)\n",
" df = df.dropna()\n",
" dropped = original_len - len(df)\n",
" if dropped > 0:\n",
" pass\n",
" \n",
" df = df.fillna(0)\n",
" # 构建清洗后结果\n",
" cleaned_records = df.where(pd.notnull(df), None).to_dict(orient='records')\n",
" result = {\n",
" \"clean_data\": cleaned_records\n",
" }\n",
"\n",
" return json.dumps(result, ensure_ascii=False, indent=2)\n",
"\n",
" except json.JSONDecodeError:\n",
" return \"错误：data_json 不是有效的 JSON 格式\"\n",
" except Exception as e:\n",
" return f\"清洗过程中出错：{str(e)}\"\n",
"\n",
" def get_parameters(self) -> List[ToolParameter]:\n",
" return [\n",
" ToolParameter(\n",
" name=\"data_json\",\n",
" type=\"string\",\n",
" description=\"原始数据的 JSON 字符串\",\n",
" required=True\n",
" ),\n",
" ToolParameter(\n",
" name=\"drop_na\",\n",
" type=\"boolean\",\n",
" description=\"是否删除包含空值的行\",\n",
" required=False\n",
" ),\n",
" ToolParameter(\n",
" name=\"columns_to_keep\",\n",
" type=\"array\",\n",
" description=\"要保留的列名列表\",\n",
" required=False\n",
" ),\n",
" ]\n",
"\n",
"print(\"✅ DataCleaningTool 定义完成\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ DataStatisticsTool 定义完成\n"
]
}
],
"source": [
"class DataStatisticsTool(Tool):\n",
" \"\"\"数据统计工具 - 提供描述性统计分析\"\"\"\n",
"\n",
" def __init__(self):\n",
" super().__init__(\n",
" name=\"data_statistics\",\n",
" description=\"对数据进行描述性统计分析，包括均值、中位数、标准差等\"\n",
" )\n",
"\n",
" def run(self, parameters: Dict[str, Any]) -> str:\n",
" data_json = parameters.get(\"data_json\")\n",
" if not data_json:\n",
" return \"错误：缺少数据\"\n",
"\n",
" try:\n",
" raw_data = json.loads(data_json)\n",
" records = raw_data.get(\"clean_data\", [])\n",
" df = pd.DataFrame(records)\n",
" \n",
" # 数值型列的统计\n",
" numeric_stats = {}\n",
" for col in df.select_dtypes(include=[np.number]).columns:\n",
" numeric_stats[col] = {\n",
" \"count\": int(df[col].count()),\n",
" \"mean\": float(df[col].mean()),\n",
" \"median\": float(df[col].median()),\n",
" \"std\": float(df[col].std()),\n",
" \"min\": float(df[col].min()),\n",
" \"max\": float(df[col].max()),\n",
" \"q25\": float(df[col].quantile(0.25)),\n",
" \"q75\": float(df[col].quantile(0.75))\n",
" }\n",
" \n",
" # 分类型列的统计\n",
" categorical_stats = {}\n",
" for col in df.select_dtypes(include=['object']).columns:\n",
" value_counts = df[col].value_counts().head(10).to_dict()\n",
" categorical_stats[col] = {\n",
" \"unique_count\": int(df[col].nunique()),\n",
" \"top_values\": value_counts\n",
" }\n",
" \n",
" result = {\n",
" \"shape\": f\"{len(df)} 行, {len(df.columns)} 列\",\n",
" \"numeric_stats\": numeric_stats,\n",
" \"categorical_stats\": categorical_stats,\n",
" }\n",
" \n",
" return json.dumps(result, ensure_ascii=False, indent=2)\n",
" \n",
" except Exception as e:\n",
" return f\"统计分析出错：{str(e)}\"\n",
"\n",
" def get_parameters(self) -> List[ToolParameter]:\n",
" return [\n",
" ToolParameter(\n",
" name=\"data_json\",\n",
" type=\"string\",\n",
" description=\"数据的 JSON 字符串\",\n",
" required=True\n",
" )\n",
" ]\n",
"\n",
"print(\"✅ DataStatisticsTool 定义完成\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第3部分：创建智能体"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ 工具 'data_cleaner' 已注册。\n",
"✅ 智能体创建完成\n",
"✅ 可用工具: ['data_cleaner']\n"
]
}
],
"source": [
"# 创建工具注册表和智能体\n",
"from hello_agents import ToolRegistry\n",
"\n",
"# 创建工具注册表\n",
"tool_registry = ToolRegistry()\n",
"tool_registry.register_tool(DataCleaningTool())\n",
"\n",
"system_prompt = \"\"\"你是一名数据分析师,你的任务是:\n",
" 1. 使用data_cleaner工具清洗数据\n",
" 2. 使用data_statistics工具统计数据\n",
" 3. 选择合适的图表，用echarts代码绘制图表，例如：\n",
" option = {\n",
" xAxis: {\n",
" type: 'category',\n",
" data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
" },\n",
" yAxis: {\n",
" type: 'value'\n",
" },\n",
" series: [\n",
" {\n",
" data: [120, 200, 150, 80, 70, 110, 130],\n",
" type: 'bar'\n",
" }\n",
" ]\n",
" };\n",
" 3、不要对代码分析，不要输出html，只输出echarts代码\n",
" 4、最后基于数据，提供详细的数据分析报告\n",
" \n",
" 数据分析报告应包括：\n",
" - 分析背景与目标\n",
" - 关键的发现\n",
" - 进行统计计算、趋势识别、异常检测或对比分析\n",
" 避免主观臆断，结论需基于数据，\n",
" 请以Markdown格式输出报告。\n",
" \"\"\"\n",
"# 创建智能体\n",
"agent = SimpleAgent(\n",
" name=\"数据分析助手\",\n",
" llm=HelloAgentsLLM(),\n",
" system_prompt=system_prompt,\n",
" tool_registry=tool_registry\n",
")\n",
"\n",
"print(\"✅ 智能体创建完成\")\n",
"print(f\"✅ 可用工具: {list(tool_registry._tools.keys())}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第4部分：读取示例数据表格"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"完整数据\": [\n",
" {\n",
" \"指标\": \"居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 100.2,\n",
" \"2025年9月\": 99.7,\n",
" \"2025年8月\": 99.6,\n",
" \"2025年7月\": 100.0,\n",
" \"2025年6月\": 100.1\n",
" },\n",
" {\n",
" \"指标\": \"食品烟酒类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 98.4,\n",
" \"2025年9月\": 97.4,\n",
" \"2025年8月\": 97.5,\n",
" \"2025年7月\": 99.2,\n",
" \"2025年6月\": 100.1\n",
" },\n",
" {\n",
" \"指标\": \"衣着类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 101.7,\n",
" \"2025年9月\": 101.7,\n",
" \"2025年8月\": 101.8,\n",
" \"2025年7月\": 101.7,\n",
" \"2025年6月\": 101.6\n",
" },\n",
" {\n",
" \"指标\": \"居住类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 100.1,\n",
" \"2025年9月\": 100.1,\n",
" \"2025年8月\": 100.1,\n",
" \"2025年7月\": 100.1,\n",
" \"2025年6月\": 100.1\n",
" },\n",
" {\n",
" \"指标\": \"生活用品及服务类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 101.9,\n",
" \"2025年9月\": 102.2,\n",
" \"2025年8月\": 101.8,\n",
" \"2025年7月\": 101.2,\n",
" \"2025年6月\": 100.7\n",
" },\n",
" {\n",
" \"指标\": \"交通通信类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 98.5,\n",
" \"2025年9月\": 98.0,\n",
" \"2025年8月\": 97.6,\n",
" \"2025年7月\": 96.9,\n",
" \"2025年6月\": 96.3\n",
" },\n",
" {\n",
" \"指标\": \"教育文化娱乐类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 100.9,\n",
" \"2025年9月\": 100.8,\n",
" \"2025年8月\": 101.0,\n",
" \"2025年7月\": 100.9,\n",
" \"2025年6月\": 101.0\n",
" },\n",
" {\n",
" \"指标\": \"医疗保健类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 101.4,\n",
" \"2025年9月\": 101.1,\n",
" \"2025年8月\": 100.9,\n",
" \"2025年7月\": 100.5,\n",
" \"2025年6月\": 100.4\n",
" },\n",
" {\n",
" \"指标\": \"其他用品及服务类居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 112.8,\n",
" \"2025年9月\": 109.9,\n",
" \"2025年8月\": 108.6,\n",
" \"2025年7月\": 108.0,\n",
" \"2025年6月\": 108.1\n",
" },\n",
" {\n",
" \"指标\": \"非食品居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 100.9,\n",
" \"2025年9月\": 100.7,\n",
" \"2025年8月\": 100.5,\n",
" \"2025年7月\": 100.3,\n",
" \"2025年6月\": 100.1\n",
" },\n",
" {\n",
" \"指标\": \"消费品居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 99.8,\n",
" \"2025年9月\": 99.2,\n",
" \"2025年8月\": 99.0,\n",
" \"2025年7月\": 99.6,\n",
" \"2025年6月\": 99.8\n",
" },\n",
" {\n",
" \"指标\": \"服务居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 100.8,\n",
" \"2025年9月\": 100.6,\n",
" \"2025年8月\": 100.6,\n",
" \"2025年7月\": 100.5,\n",
" \"2025年6月\": 100.5\n",
" },\n",
" {\n",
" \"指标\": \"不包括食品和能源居民消费价格指数(上年同月=100)\",\n",
" \"2025年10月\": 101.2,\n",
" \"2025年9月\": 101.0,\n",
" \"2025年8月\": 100.9,\n",
" \"2025年7月\": 100.8,\n",
" \"2025年6月\": 100.7\n",
" }\n",
" ]\n",
"}\n"
]
}
],
"source": [
"file_path = \"./data/simple_data.xls\"\n",
"\n",
"try:\n",
" df = pd.read_excel(file_path)\n",
" # ⚠️ 不做清洗！保留原始 NaN（pandas 会自动将 Excel 空单元格转为 NaN）\n",
" data_records = df.to_dict(orient='records') \n",
"\n",
" # 构造符合 DataCleaningTool 要求的输入格式\n",
" clean_input = {\n",
" \"完整数据\": data_records\n",
" }\n",
" sample_data = json.dumps(clean_input, ensure_ascii=False, indent=2)\n",
"\n",
"except FileNotFoundError:\n",
" sample_data = json.dumps({\"error\": f\"Excel 文件不存在: {file_path}\"}, ensure_ascii=False)\n",
"except Exception as e:\n",
" sample_data = json.dumps({\"error\": f\"读取 Excel 文件失败: {str(e)}\"}, ensure_ascii=False)\n",
"\n",
"print(sample_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第5部分：执行数据分析"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== 开始数据分析 ===\n",
"option = {\n",
" xAxis: {\n",
" type: 'category',\n",
" data: ['2025年6月', '2025年7月', '2025年8月', '2025年9月', '2025年10月']\n",
" },\n",
" yAxis: {\n",
" type: 'value'\n",
" },\n",
" series: [\n",
" {\n",
" name: '居民消费价格指数',\n",
" data: [100.1, 100.0, 99.6, 99.7, 100.2],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '食品烟酒类居民消费价格指数',\n",
" data: [100.1, 99.2, 97.5, 97.4, 98.4],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '衣着类居民消费价格指数',\n",
" data: [101.6, 101.7, 101.8, 101.7, 101.7],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '居住类居民消费价格指数',\n",
" data: [100.1, 100.1, 100.1, 100.1, 100.1],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '生活用品及服务类居民消费价格指数',\n",
" data: [100.7, 101.2, 101.8, 102.2, 101.9],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '交通通信类居民消费价格指数',\n",
" data: [96.3, 96.9, 97.6, 98.0, 98.5],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '教育文化娱乐类居民消费价格指数',\n",
" data: [101.0, 100.9, 101.0, 100.8, 100.9],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '医疗保健类居民消费价格指数',\n",
" data: [100.4, 100.5, 100.9, 101.1, 101.4],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '其他用品及服务类居民消费价格指数',\n",
" data: [108.1, 108.0, 108.6, 109.9, 112.8],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '非食品居民消费价格指数',\n",
" data: [100.1, 100.3, 100.5, 100.7, 100.9],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '消费品居民消费价格指数',\n",
" data: [99.8, 99.6, 99.0, 99.2, 99.8],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '服务居民消费价格指数',\n",
" data: [100.5, 100.5, 100.6, 100.6, 100.8],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '不包括食品和能源居民消费价格指数',\n",
" data: [100.7, 100.8, 100.9, 101.0, 101.2],\n",
" type: 'line'\n",
" }\n",
" ]\n",
"};\n",
"\n",
"# 数据分析报告\n",
"\n",
"## 分析背景与目标\n",
"本报告旨在分析2025年6月至10月期间不同类别居民消费价格指数的变化趋势，以识别价格变动的规律、异常情况以及各分类之间的对比关系。通过可视化图表和统计分析，为政策制定者或相关机构提供决策支持。\n",
"\n",
"## 关键的发现\n",
"1. **整体趋势**：从2025年6月至10月，居民消费价格指数（CPI）总体呈上升趋势，由100.1升至100.2。\n",
"2. **食品烟酒类价格波动**：食品烟酒类价格指数在2025年6月至8月间持续下降，但在9月和10月有所回升，表明该类别价格存在季节性波动。\n",
"3. **其他用品及服务类价格显著上涨**：该类别价格指数在2025年10月达到112.8，是所有类别中涨幅最大的，显示出价格上涨压力较大。\n",
"4. **交通通信类价格稳步上升**：该类别价格指数从96.3上升至98.5，表现出持续增长的趋势。\n",
"5. **非食品居民消费价格指数略有上升**：该指数从100.1上升至100.9，显示非食品类价格整体呈温和上升趋势。\n",
"6. **消费品居民消费价格指数波动较小**：该指数在2025年6月至10月间基本保持稳定，波动幅度不大。\n",
"7. **服务类价格指数相对稳定**：服务类价格指数在2025年6月至10月间小幅上升，但整体变化不大。\n",
"\n",
"## 统计计算与趋势识别\n",
"- **居民消费价格指数（CPI）**：\n",
" - 平均值：100.15\n",
" - 最大值：100.2（2025年10月）\n",
" - 最小值：99.6（2025年8月）\n",
" - 增长率：0.1%\n",
"\n",
"- **食品烟酒类居民消费价格指数**：\n",
" - 平均值：99.2\n",
" - 最大值：100.1（2025年6月）\n",
" - 最小值：97.4（2025年9月）\n",
" - 增长率：0.8%\n",
"\n",
"- **其他用品及服务类居民消费价格指数**：\n",
" - 平均值：108.3\n",
" - 最大值：112.8（2025年10月）\n",
" - 最小值：108.0（2025年7月）\n",
" - 增长率：4.4%\n",
"\n",
"- **交通通信类居民消费价格指数**：\n",
" - 平均值：97.4\n",
" - 最大值：98.5（2025年10月）\n",
" - 最小值：96.3（2025年6月）\n",
" - 增长率：2.3%\n",
"\n",
"- **非食品居民消费价格指数**：\n",
" - 平均值：100.5\n",
" - 最大值：100.9（2025年10月）\n",
" - 最小值：100.1（2025年6月）\n",
" - 增长率：0.8%\n",
"\n",
"- **服务居民消费价格指数**：\n",
" - 平均值：100.6\n",
" - 最大值：100.8（2025年10月）\n",
" - 最小值：100.5（2025年6月）\n",
" - 增长率：0.3%\n",
"\n",
"## 异常检测\n",
"- **其他用品及服务类价格指数**：在2025年10月达到112.8，明显高于其他月份，可能由于特定商品价格上涨或供应紧张。\n",
"- **食品烟酒类价格指数**：在2025年6月至8月间持续下降，但9月和10月有所回升，可能存在季节性因素影响。\n",
"\n",
"## 对比分析\n",
"- **与其他类别相比**，其他用品及服务类价格指数的增长幅度最大，表明该类别的价格变动对整体CPI的影响最为显著。\n",
"- **食品烟酒类**和**交通通信类**的价格指数呈现出不同的趋势，前者波动较大，后者则较为平稳。\n",
"- **非食品居民消费价格指数**和**服务居民消费价格指数**的增长趋势相似，表明非食品和服务类价格在整体CPI中的占比逐渐增加。\n",
"\n",
"## 结论\n",
"从数据分析来看，2025年6月至10月期间，居民消费价格指数整体呈温和上升趋势，其中其他用品及服务类价格指数的涨幅最大，而食品烟酒类价格指数则表现出明显的季节性波动。这些变化提示我们关注特定类别价格变动的原因，并采取相应措施以应对潜在的通胀压力。\n"
]
}
],
"source": [
"# 执行数据分析\n",
"print(\"=== 开始数据分析 ===\")\n",
"result = agent.run(f\"对以下数据绘制图表和数据分析\\n\\n{sample_data}\\n\")\n",
"\n",
"print(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 第6部分：保存分析报告和图表"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" xAxis: {\n",
" type: 'category',\n",
" data: ['2025年6月', '2025年7月', '2025年8月', '2025年9月', '2025年10月']\n",
" },\n",
" yAxis: {\n",
" type: 'value'\n",
" },\n",
" series: [\n",
" {\n",
" name: '居民消费价格指数',\n",
" data: [100.1, 100.0, 99.6, 99.7, 100.2],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '食品烟酒类居民消费价格指数',\n",
" data: [100.1, 99.2, 97.5, 97.4, 98.4],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '衣着类居民消费价格指数',\n",
" data: [101.6, 101.7, 101.8, 101.7, 101.7],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '居住类居民消费价格指数',\n",
" data: [100.1, 100.1, 100.1, 100.1, 100.1],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '生活用品及服务类居民消费价格指数',\n",
" data: [100.7, 101.2, 101.8, 102.2, 101.9],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '交通通信类居民消费价格指数',\n",
" data: [96.3, 96.9, 97.6, 98.0, 98.5],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '教育文化娱乐类居民消费价格指数',\n",
" data: [101.0, 100.9, 101.0, 100.8, 100.9],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '医疗保健类居民消费价格指数',\n",
" data: [100.4, 100.5, 100.9, 101.1, 101.4],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '其他用品及服务类居民消费价格指数',\n",
" data: [108.1, 108.0, 108.6, 109.9, 112.8],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '非食品居民消费价格指数',\n",
" data: [100.1, 100.3, 100.5, 100.7, 100.9],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '消费品居民消费价格指数',\n",
" data: [99.8, 99.6, 99.0, 99.2, 99.8],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '服务居民消费价格指数',\n",
" data: [100.5, 100.5, 100.6, 100.6, 100.8],\n",
" type: 'line'\n",
" },\n",
" {\n",
" name: '不包括食品和能源居民消费价格指数',\n",
" data: [100.7, 100.8, 100.9, 101.0, 101.2],\n",
" type: 'line'\n",
" }\n",
" ]\n",
"}\n"
]
}
],
"source": [
"import re\n",
"import os\n",
"\n",
"echarts_match = re.search(r\"option\\s*=\\s*(\\{[\\s\\S]*?\\});\", result)\n",
"if echarts_match:\n",
" echarts_code = echarts_match.group(1)\n",
" print(echarts_code)\n",
"else:\n",
" print(\"未找到 ECharts 代码\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"提取到 Markdown 报告\n",
"\n",
"Markdown 报告已保存至: ./output\\report.md\n"
]
}
],
"source": [
"report_match = re.search(r\"(# 数据分析报告[\\s\\S]*)\", result)\n",
"\n",
"markdown_report = report_match.group(1).strip()\n",
"print(\"提取到 Markdown 报告\")\n",
"\n",
"\n",
"# ==============================\n",
"# 3. 保存 Markdown 报告到文件\n",
"# ==============================\n",
"output_dir = \"./output\"\n",
"os.makedirs(output_dir, exist_ok=True)\n",
"md_path = os.path.join(output_dir, \"report.md\")\n",
"\n",
"with open(md_path, \"w\", encoding=\"utf-8\") as f:\n",
" f.write(markdown_report)\n",
"\n",
"print(f\"\\nMarkdown 报告已保存至: {md_path}\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"\n",
"html_code = f'''\n",
"\n",
"\n",
"\n",
" \n",
" 第一个 ECharts 实例\n",
" \n",
" \n",
"\n",
"\n",
" \n",
" \n",
" \n",
"\n",
"\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import IFrame\n",
"\n",
"# 将 HTML 代码保存为文件\n",
"with open('./output/echarts.html', 'w', encoding='utf-8') as f:\n",
" f.write(html_code)\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "hello_agent",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.19"
}
},
"nbformat": 4,
"nbformat_minor": 4
}