# auth_kv

key: codewhisperer:odic:device-registration
value:
  {"client_id":"h-hggX4vR1ZzCvLaXp_J2nVzLWVhc3QtMQ","client_secret":"eyJraWQiOiJrZXktMTU2NDAyODA5OSIsImFsZyI6IkhTMzg0In0.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.WnFerF7j6xb2mdQtCzF7t7FOXhMspwFH30dICDUJvllQBiwyA1eE7EGKIG19ZtnY","client_secret_expires_at":"2026-01-26T22:40:15Z","region":"us-east-1","oauth_flow":"DeviceCode","scopes":["codewhisperer:completions","codewhisperer:analysis","codewhisperer:conversations"]}


key: codewhisperer:odic:token
value:
  {"access_token":"aoaAAAAAGlV7Q0MIYLT2J9xyE0JJe3WmJmSk-jCHB-9Igf5ZC1uOrlZuL2e-WLJfEJGryURjdKF9nG9H-nj0euQ5UBkc0:MGYCMQDDbbBj7TMflMs5PkLTupPlkCohltNil8v9KT1NP4Ilh6Rbsb5BSxCam1Aolz1YOEgCMQD0aUOOTciVXQzGpDG/jopkgYLCmp83SnGZpFyBlpHT6ET8nvsvuzjo2gym+jZfInY","expires_at":"2026-01-01T03:42:05.735194798Z","refresh_token":"aorAAAAAGl2s84XCX_lrQG7UQrQdGdUae4UgEwHoIoZ9ZZjA5DnATNHKyY0VNG8PlZIF-xzbZXZfrabuQPWL2ZzWIBkc0:MGUCMGgaID9sPbteLBF+kiv5TozorJ2FgvGxAvlNrv+TSombgInjSbENlr83PaIXcTDIqwIxAKqTN20AqEEHikqMyucTHCJYrfLKeeN3luRBU3P1tPAB9kdspea4G4RX56hcaXyTfg","region":"us-east-1","start_url":null,"oauth_flow":"DeviceCode","scopes":["codewhisperer:completions","codewhisperer:analysis","codewhisperer:conversations"]}


# state

key: telemetry-cognito-credentials
value:
  {"access_key_id":"ASIAWEKYMYUKCITB7JD5","expiration":"2026-01-01T03:42:06Z","secret_key":"g73dSk6R/CGGtAeEVe8bMpSYru6GzETwW31geM7b","session_token":"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"}


# conversations

key: /root/q
value:
  {"conversation_id":"eaf7d180-d773-4b42-ae77-db5bd43fca0d","next_message":null,"history":[{"user":{"additional_context":"","env_context":{"env_state":{"operating_system":"linux","current_working_directory":"/root/q","environment_variables":[]}},"content":{"Prompt":{"prompt":"install yandex cloud cli tool"}},"timestamp":"2025-10-28T15:40:58.201916109-07:00","images":null},"assistant":{"ToolUse":{"message_id":"45b73b01-c974-4f1e-b482-924f9fa8a012","content":"I'll help you install the Yandex Cloud CLI tool. Let me check the installation method for your Linux system.","tool_uses":[{"id":"tooluse_pTAplevcSHqqum8E-PYSZA","name":"execute_bash","orig_name":"execute_bash","args":{"command":"curl -sSL https://storage.yandexcloud.net/yandexcloud-yc/install.sh | bash","summary":"Install Yandex Cloud CLI using the official installation script"},"orig_args":{"command":"curl -sSL https://storage.yandexcloud.net/yandexcloud-yc/install.sh | bash","summary":"Install Yandex Cloud CLI using the official installation script"}}]}},"request_metadata":{"request_id":"166af46d-658d-4731-aad5-59e5f88cd3e8","message_id":"45b73b01-c974-4f1e-b482-924f9fa8a012","request_start_timestamp_ms":1761691258210,"stream_end_timestamp_ms":1761691260952,"time_to_first_chunk":{"secs":1,"nanos":653523508},"time_between_chunks":[{"secs":0,"nanos":87420},{"secs":0,"nanos":151032},{"secs":0,"nanos":406535},{"secs":0,"nanos":944406},{"secs":0,"nanos":883537},{"secs":0,"nanos":928609},{"secs":0,"nanos":308416},{"secs":0,"nanos":176542},{"secs":0,"nanos":314241},{"secs":0,"nanos":65934321},{"secs":0,"nanos":51356},{"secs":0,"nanos":609841651},{"secs":0,"nanos":296859},{"secs":0,"nanos":566638},{"secs":0,"nanos":964582},{"secs":0,"nanos":607426},{"secs":0,"nanos":648989},{"secs":0,"nanos":514429},{"secs":0,"nanos":705127},{"secs":0,"nanos":768566},{"secs":0,"nanos":621089},{"secs":0,"nanos":922814},{"secs":0,"nanos":760030},{"secs":0,"nanos":786738},{"secs":0,"nanos":494997},{"secs":0,"nanos":115102},{"secs":0,"nanos":354255481},{"secs":0,"nanos":1603884},{"secs":0,"nanos":2498357},{"secs":0,"nanos":1144857},{"secs":0,"nanos":3547212},{"secs":0,"nanos":29349733},{"secs":0,"nanos":798651},{"secs":0,"nanos":602932},{"secs":0,"nanos":421873},{"secs":0,"nanos":839473},{"secs":0,"nanos":601264},{"secs":0,"nanos":609261},{"secs":0,"nanos":734764},{"secs":0,"nanos":2131039},{"secs":0,"nanos":561751},{"secs":0,"nanos":233}],"user_prompt_length":188,"response_size":275,"chat_conversation_type":"ToolUse","tool_use_ids_and_names":[["tooluse_pTAplevcSHqqum8E-PYSZA","execute_bash"]],"model_id":"claude-sonnet-4","message_meta_tags":[]}}],"valid_history_range":[0,0],"transcript":["> install yandex cloud cli tool","I'll help you install the Yandex Cloud CLI tool. Let me check the installation method for your Linux system.\n[Tool uses: execute_bash]"],"tools":{"native___":[{"ToolSpecification":{"name":"fs_read","description":"Tool for reading files, directories and images. Always provide an 'operations' array.\n\nFor single operation: provide array with one element.\nFor batch operations: provide array with multiple elements.\n\nAvailable modes:\n- Line: Read lines from a file\n- Directory: List directory contents\n- Search: Search for patterns in files\n- Image: Read and process images\n\nExamples:\n1. Single: {\"operations\": [{\"mode\": \"Line\", \"path\": \"/file.txt\"}]}\n2. Batch: {\"operations\": [{\"mode\": \"Line\", \"path\": \"/file1.txt\"}, {\"mode\": \"Search\", \"path\": \"/file2.txt\", \"pattern\": \"test\"}]}","input_schema":{"json":{"properties":{"operations":{"minItems":1,"type":"array","description":"Array of operations to execute. Provide one element for single operation, multiple for batch.","items":{"type":"object","properties":{"pattern":{"description":"Pattern to search for (required, for Search mode). Case insensitive. The pattern matching is performed per line.","type":"string"},"image_paths":{"items":{"type":"string"},"description":"List of paths to the images. This is currently supported by the Image mode.","type":"array"},"mode":{"type":"string","enum":["Line","Directory","Search","Image"],"description":"The operation mode to run in: `Line`, `Directory`, `Search`. `Line` and `Search` are only for text files, and `Directory` is only for directories. `Image` is for image files, in this mode `image_paths` is required."},"path":{"type":"string","description":"Path to the file or directory. The path should be absolute, or otherwise start with ~ for the user's home (required for Line, Directory, Search modes)."},"start_line":{"default":1,"description":"Starting line number (optional, for Line mode). A negative index represents a line number starting from the end of the file.","type":"integer"},"end_line":{"description":"Ending line number (optional, for Line mode). A negative index represents a line number starting from the end of the file.","type":"integer","default":-1},"context_lines":{"description":"Number of context lines around search results (optional, for Search mode)","type":"integer","default":2},"depth":{"default":0,"type":"integer","description":"Depth of a recursive directory listing (optional, for Directory mode)"}},"required":["mode"]}},"summary":{"type":"string","description":"Optional description of the purpose of this batch operation (mainly useful for multiple operations)"}},"required":["operations"],"type":"object"}}}},{"ToolSpecification":{"name":"fs_write","description":"A tool for creating and editing files\n * The `create` command will override the file at `path` if it already exists as a file, and otherwise create a new file\n * The `append` command will add content to the end of an existing file, automatically adding a newline if the file doesn't end with one. The file must exist.\n Notes for using the `str_replace` command:\n * The `old_str` parameter should match EXACTLY one or more consecutive lines from the original file. Be mindful of whitespaces!\n * If the `old_str` parameter is not unique in the file, the replacement will not be performed. Make sure to include enough context in `old_str` to make it unique\n * The `new_str` parameter should contain the edited lines that should replace the `old_str`.","input_schema":{"json":{"required":["command","path"],"properties":{"command":{"type":"string","description":"The commands to run. Allowed options are: `create`, `str_replace`, `insert`, `append`.","enum":["create","str_replace","insert","append"]},"file_text":{"type":"string","description":"Required parameter of `create` command, with the content of the file to be created."},"summary":{"type":"string","description":"A brief explanation of what the file change does or why it's being made."},"insert_line":{"description":"Required parameter of `insert` command. The `new_str` will be inserted AFTER the line `insert_line` of `path`.","type":"integer"},"old_str":{"description":"Required parameter of `str_replace` command containing the string in `path` to replace.","type":"string"},"new_str":{"description":"Required parameter of `str_replace` command containing the new string. Required parameter of `insert` command containing the string to insert. Required parameter of `append` command containing the content to append to the file.","type":"string"},"path":{"description":"Absolute path to file or directory, e.g. `/repo/file.py` or `/repo`.","type":"string"}},"type":"object"}}}},{"ToolSpecification":{"name":"report_issue","description":"Opens the browser to a pre-filled gh (GitHub) issue template to report chat issues, bugs, or feature requests. Pre-filled information includes the conversation transcript, chat context, and chat request IDs from the service.","input_schema":{"json":{"properties":{"expected_behavior":{"description":"Optional: The expected chat behavior or action that did not happen.","type":"string"},"title":{"type":"string","description":"The title of the GitHub issue."},"actual_behavior":{"description":"Optional: The actual chat behavior that happened and demonstrates the issue or lack of a feature.","type":"string"},"steps_to_reproduce":{"type":"string","description":"Optional: Previous user chat requests or steps that were taken that may have resulted in the issue or error response."}},"required":["title"],"type":"object"}}}},{"ToolSpecification":{"name":"use_aws","description":"Make an AWS CLI api call with the specified service, operation, and parameters. All arguments MUST conform to the AWS CLI specification. Should the output of the invocation indicate a malformed command, invoke help to obtain the the correct command.","input_schema":{"json":{"type":"object","required":["region","service_name","operation_name","label"],"properties":{"label":{"type":"string","description":"Human readable description of the api that is being called."},"service_name":{"type":"string","description":"The name of the AWS service. If you want to query s3, you should use s3api if possible."},"parameters":{"type":"object","description":"The parameters for the operation. The parameter keys MUST conform to the AWS CLI specification. You should prefer to use JSON Syntax over shorthand syntax wherever possible. For parameters that are booleans, prioritize using flags with no value. Denote these flags with flag names as key and an empty string as their value. You should also prefer kebab case."},"region":{"type":"string","description":"Region name for calling the operation on AWS."},"profile_name":{"type":"string","description":"Optional: AWS profile name to use from ~/.aws/credentials. Defaults to default profile if not specified."},"operation_name":{"type":"string","description":"The name of the operation to perform."}}}}}},{"ToolSpecification":{"name":"introspect","description":"ALWAYS use this tool when users ask ANY question about Q CLI itself, its capabilities, features, commands, or functionality. This includes questions like 'Can you...', 'Do you have...', 'How do I...', 'What can you do...', or any question about Q's abilities. When mentioning commands in your response, always prefix them with '/' (e.g., '/save', '/load', '/context'). CRITICAL: Only provide information explicitly documented in Q CLI documentation. If details about any tool, feature, or command are not documented, clearly state the information is not available rather than generating assumptions.","input_schema":{"json":{"type":"object","properties":{"query":{"description":"The user's question about Q CLI usage, features, or capabilities","type":"string"}},"required":[]}}}},{"ToolSpecification":{"name":"execute_bash","description":"Execute the specified bash command.","input_schema":{"json":{"type":"object","properties":{"summary":{"description":"A brief explanation of what the command does","type":"string"},"command":{"description":"Bash command to execute","type":"string"}},"required":["command"]}}}},{"ToolSpecification":{"name":"dummy","description":"This is a dummy tool. If you are seeing this that means the tool associated with this tool call is not in the list of available tools. This could be because a wrong tool name was supplied or the list of tools has changed since the conversation has started. Do not show this when user asks you to list tools.","input_schema":{"json":{"properties":{},"required":[],"type":"object"}}}}]},"context_manager":{"max_context_files_size":150000,"current_profile":"q_cli_default","paths":["AmazonQ.md","AGENTS.md","README.md",".amazonq/rules/**/*.md"],"hooks":{}},"context_message_length":null,"latest_summary":null,"model_info":{"model_name":"claude-sonnet-4","model_id":"claude-sonnet-4","context_window_tokens":200000},"file_line_tracker":{},"checkpoint_manager":null,"mcp_enabled":true}


key: /mnt/pikpak-racknerd01/BF3
value:
  {"conversation_id":"f9a4076e-f122-4104-b2ef-8eea0d87579f","next_message":null,"history":[{"user":{"additional_context":"","env_context":{"env_state":{"operating_system":"linux","current_working_directory":"/mnt/pikpak-racknerd01/BF3","environment_variables":[]}},"content":{"Prompt":{"prompt":"对csv文件生成统计分析文件"}},"timestamp":"2025-12-07T17:19:36.200163193-08:00","images":null},"assistant":{"ToolUse":{"message_id":"bec28178-a33c-4725-aea7-3bc3d2e466b2","content":"I'll help you generate statistical analysis files for CSV data. Let me first check what CSV files are available in your current directory.","tool_uses":[{"id":"tooluse_je6YHeQLRtyYwk05M1fAhw","name":"fs_read","orig_name":"fs_read","args":{"operations":[{"mode":"Directory","path":"/mnt/pikpak-racknerd01/BF3"}],"summary":"Check for CSV files in current directory"},"orig_args":{"operations":[{"mode":"Directory","path":"/mnt/pikpak-racknerd01/BF3"}],"summary":"Check for CSV files in current directory"}}]}},"request_metadata":{"request_id":"b5386590-fb9d-4fb0-9631-c5a3bab85da0","message_id":"bec28178-a33c-4725-aea7-3bc3d2e466b2","request_start_timestamp_ms":1765156776205,"stream_end_timestamp_ms":1765156779277,"time_to_first_chunk":{"secs":1,"nanos":711353426},"time_between_chunks":[{"secs":0,"nanos":170887},{"secs":0,"nanos":30103},{"secs":0,"nanos":33589603},{"secs":0,"nanos":99572245},{"secs":0,"nanos":55176690},{"secs":0,"nanos":46635294},{"secs":0,"nanos":77366982},{"secs":0,"nanos":47485191},{"secs":0,"nanos":56607787},{"secs":0,"nanos":37896261},{"secs":0,"nanos":74302490},{"secs":0,"nanos":15760542},{"secs":0,"nanos":67191166},{"secs":0,"nanos":136331860},{"secs":0,"nanos":324142913},{"secs":0,"nanos":373356},{"secs":0,"nanos":597926},{"secs":0,"nanos":755001},{"secs":0,"nanos":774294},{"secs":0,"nanos":606877},{"secs":0,"nanos":828176},{"secs":0,"nanos":552109},{"secs":0,"nanos":594175},{"secs":0,"nanos":231911523},{"secs":0,"nanos":351194},{"secs":0,"nanos":712337},{"secs":0,"nanos":39698296},{"secs":0,"nanos":2982064},{"secs":0,"nanos":479830},{"secs":0,"nanos":1493043},{"secs":0,"nanos":4737976},{"secs":0,"nanos":95200},{"secs":0,"nanos":173}],"user_prompt_length":194,"response_size":267,"chat_conversation_type":"ToolUse","tool_use_ids_and_names":[["tooluse_je6YHeQLRtyYwk05M1fAhw","fs_read"]],"model_id":"claude-sonnet-4","message_meta_tags":[]}},{"user":{"additional_context":"","env_context":{"env_state":{"operating_system":"linux","current_working_directory":"/mnt/pikpak-racknerd01/BF3","environment_variables":[]}},"content":{"ToolUseResults":{"tool_use_results":[{"tool_use_id":"tooluse_je6YHeQLRtyYwk05M1fAhw","content":[{"Text":"drwxr-xr-x 1 0 0 0 Oct 09 00:38 /mnt/pikpak-racknerd01/BF3/.claude\n-rw-r--r-- 1 0 0 791774471 Aug 29 14:08 /mnt/pikpak-racknerd01/BF3/6M-Household-Registration-Data-China-csv-2020.csv\n-rw-r--r-- 1 0 0 256796490 Aug 21 12:48 /mnt/pikpak-racknerd01/BF3/6M-Household-Registration-Data-China-csv-2020.zip\ndrwxr-xr-x 1 0 0 0 Aug 19 11:33 /mnt/pikpak-racknerd01/BF3/BF_CDN"}],"status":"Success"}]}},"timestamp":null,"images":null},"assistant":{"ToolUse":{"message_id":"e064ef21-373d-410e-82d0-b99c3a68d6ff","content":"I found a CSV file: `6M-Household-Registration-Data-China-csv-2020.csv`. Let me examine its structure first to understand the data format.","tool_uses":[{"id":"tooluse_FyDzuOI1SxKXLu9Rxqp98g","name":"fs_read","orig_name":"fs_read","args":{"summary":"Examine CSV file structure","operations":[{"mode":"Line","path":"/mnt/pikpak-racknerd01/BF3/6M-Household-Registration-Data-China-csv-2020.csv","start_line":1,"end_line":5}]},"orig_args":{"summary":"Examine CSV file 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I'll create a Python script to generate comprehensive statistical analysis for this Chinese household registration data:","tool_uses":[{"id":"tooluse_kibFkheeQp-yeCP0RnEwmA","name":"fs_write","orig_name":"fs_write","args":{"command":"create","path":"/mnt/pikpak-racknerd01/BF3/csv_stats_analyzer.py","summary":"Create statistical analysis script for CSV data","file_text":"#!/usr/bin/env python3\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\nimport re\n\ndef analyze_csv(file_path):\n    # 读取CSV文件\n    df = pd.read_csv(file_path)\n    \n    # 基本统计信息\n    stats = {\n        '总记录数': len(df),\n        '列数': len(df.columns),\n        '列名': list(df.columns)\n    }\n    \n    # 性别分布\n    gender_stats = df['性别'].value_counts().to_dict()\n    \n    # 年龄分析（基于出生日期）\n    df['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d')\n    current_year = datetime.now().year\n    df['年龄'] = current_year - df['出生日期'].dt.year\n    \n    age_stats = {\n        '平均年龄': round(df['年龄'].mean(), 2),\n        '最小年龄': df['年龄'].min(),\n        '最大年龄': df['年龄'].max(),\n        '年龄中位数': df['年龄'].median()\n    }\n    \n    # 地区分布（提取省份）\n    df['省份'] = df['地址'].str.extract(r'(.*?省)')\n    province_stats = df['省份'].value_counts().head(10).to_dict()\n    \n    # 生成报告\n    report = f\"\"\"\n# CSV数据统计分析报告\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n## 基本信息\n- 总记录数: {stats['总记录数']:,}\n- 数据列数: {stats['列数']}\n- 列名: {', '.join(stats['列名'])}\n\n## 性别分布\n\"\"\"\n    for gender, count in gender_stats.items():\n        percentage = (count / stats['总记录数']) * 100\n        report += f\"- {gender}: {count:,} ({percentage:.1f}%)\\n\"\n    \n    report += f\"\"\"\n## 年龄统计\n- 平均年龄: {age_stats['平均年龄']}岁\n- 年龄范围: {age_stats['最小年龄']}-{age_stats['最大年龄']}岁\n- 年龄中位数: {age_stats['年龄中位数']}岁\n\n## 地区分布（前10省份）\n\"\"\"\n    for province, count in province_stats.items():\n        if province:\n            percentage = (count / stats['总记录数']) * 100\n            report += f\"- {province}: {count:,} ({percentage:.1f}%)\\n\"\n    \n    return report, df\n\n# 执行分析\ncsv_file = '6M-Household-Registration-Data-China-csv-2020.csv'\nreport, data = analyze_csv(csv_file)\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 生成详细统计CSV\nsummary_stats = {\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄', '最小年龄', '最大年龄'],\n    '数值': [\n        len(data),\n        len(data[data['性别'] == '男']),\n        len(data[data['性别'] == '女']),\n        round(data['年龄'].mean(), 2),\n        data['年龄'].min(),\n        data['年龄'].max()\n    ]\n}\n\npd.DataFrame(summary_stats).to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\nprint(\"分析完成！生成文件：统计分析报告.md, 统计摘要.csv\")"},"orig_args":{"command":"create","path":"/mnt/pikpak-racknerd01/BF3/csv_stats_analyzer.py","summary":"Create statistical analysis script for CSV data","file_text":"#!/usr/bin/env python3\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\nimport re\n\ndef analyze_csv(file_path):\n    # 读取CSV文件\n    df = pd.read_csv(file_path)\n    \n    # 基本统计信息\n    stats = {\n        '总记录数': len(df),\n        '列数': len(df.columns),\n        '列名': list(df.columns)\n    }\n    \n    # 性别分布\n    gender_stats = df['性别'].value_counts().to_dict()\n    \n    # 年龄分析（基于出生日期）\n    df['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d')\n    current_year = datetime.now().year\n    df['年龄'] = current_year - df['出生日期'].dt.year\n    \n    age_stats = {\n        '平均年龄': round(df['年龄'].mean(), 2),\n        '最小年龄': df['年龄'].min(),\n        '最大年龄': df['年龄'].max(),\n        '年龄中位数': df['年龄'].median()\n    }\n    \n    # 地区分布（提取省份）\n    df['省份'] = df['地址'].str.extract(r'(.*?省)')\n    province_stats = df['省份'].value_counts().head(10).to_dict()\n    \n    # 生成报告\n    report = f\"\"\"\n# CSV数据统计分析报告\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n## 基本信息\n- 总记录数: {stats['总记录数']:,}\n- 数据列数: {stats['列数']}\n- 列名: {', '.join(stats['列名'])}\n\n## 性别分布\n\"\"\"\n    for gender, count in gender_stats.items():\n        percentage = (count / stats['总记录数']) * 100\n        report += f\"- {gender}: {count:,} ({percentage:.1f}%)\\n\"\n    \n    report += f\"\"\"\n## 年龄统计\n- 平均年龄: {age_stats['平均年龄']}岁\n- 年龄范围: {age_stats['最小年龄']}-{age_stats['最大年龄']}岁\n- 年龄中位数: {age_stats['年龄中位数']}岁\n\n## 地区分布（前10省份）\n\"\"\"\n    for province, count in province_stats.items():\n        if province:\n            percentage = (count / stats['总记录数']) * 100\n            report += f\"- {province}: {count:,} ({percentage:.1f}%)\\n\"\n    \n    return report, df\n\n# 执行分析\ncsv_file = '6M-Household-Registration-Data-China-csv-2020.csv'\nreport, data = analyze_csv(csv_file)\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 生成详细统计CSV\nsummary_stats = {\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄', '最小年龄', '最大年龄'],\n    '数值': [\n        len(data),\n        len(data[data['性别'] == '男']),\n        len(data[data['性别'] == '女']),\n        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统计摘要.csv\")"}}]}},"request_metadata":{"request_id":"1cbb4448-da11-451a-8140-1b261be6de27","message_id":"c8b2ce09-bec1-4780-bca9-4c813419dd98","request_start_timestamp_ms":1765156873133,"stream_end_timestamp_ms":1765156886782,"time_to_first_chunk":{"secs":2,"nanos":2475268},"time_between_chunks":[{"secs":0,"nanos":590897},{"secs":0,"nanos":11792},{"secs":0,"nanos":35779905},{"secs":0,"nanos":45168150},{"secs":0,"nanos":103776204},{"secs":0,"nanos":47274914},{"secs":0,"nanos":45985254},{"secs":0,"nanos":234426682},{"secs":0,"nanos":232058133},{"secs":0,"nanos":352332},{"secs":0,"nanos":289484},{"secs":0,"nanos":532171248},{"secs":0,"nanos":101223885},{"secs":0,"nanos":12477690},{"secs":0,"nanos":161553},{"secs":0,"nanos":7107675},{"secs":0,"nanos":1290274},{"secs":0,"nanos":2328185},{"secs":0,"nanos":1830690},{"secs":0,"nanos":7037950},{"secs":0,"nanos":1334289},{"secs":0,"nanos":8318224},{"secs":0,"nanos":31134677},{"secs":0,"nanos":123732},{"secs":0,"nanos":54708},{"secs":0,"nanos":207961},{"secs":0,"nanos":214334},{"secs":9,"nanos":942881140},{"secs":0,"nanos":26058793},{"secs":0,"nanos":64678},{"secs":0,"nanos":665873},{"secs":0,"nanos":622329},{"secs":0,"nanos":374060},{"secs":0,"nanos":1301248},{"secs":0,"nanos":67359},{"secs":0,"nanos":544912},{"secs":0,"nanos":567955},{"secs":0,"nanos":763738},{"secs":0,"nanos":525516},{"secs":0,"nanos":499307},{"secs":0,"nanos":882781},{"secs":0,"nanos":512934},{"secs":0,"nanos":621464},{"secs":0,"nanos":817947},{"secs":0,"nanos":420315},{"secs":0,"nanos":718454},{"secs":0,"nanos":546709},{"secs":0,"nanos":735808},{"secs":0,"nanos":35518693},{"secs":0,"nanos":34953},{"secs":0,"nanos":9143},{"secs":0,"nanos":7740},{"secs":0,"nanos":26052},{"secs":0,"nanos":94594},{"secs":0,"nanos":11281},{"secs":0,"nanos":18820},{"secs":0,"nanos":18727},{"secs":0,"nanos":23621},{"secs":0,"nanos":8787},{"secs":0,"nanos":14811},{"secs":0,"nanos":15192},{"secs":0,"nanos":25718332},{"secs":0,"nanos":47489},{"secs":0,"nanos":14488},{"secs":0,"nanos":11537},{"secs":0,"nanos":39184},{"secs":0,"nanos":48273},{"secs":0,"nanos":77597},{"secs":0,"nanos":474463},{"secs":0,"nanos":19532},{"secs":0,"nanos":12611},{"secs":0,"nanos":26054},{"secs":0,"nanos":16948},{"secs":0,"nanos":10852},{"secs":0,"nanos":12028},{"secs":0,"nanos":91002},{"secs":0,"nanos":352136},{"secs":0,"nanos":186034},{"secs":0,"nanos":85675},{"secs":0,"nanos":314916},{"secs":0,"nanos":20519},{"secs":0,"nanos":12619},{"secs":0,"nanos":10426},{"secs":0,"nanos":59487},{"secs":0,"nanos":17441},{"secs":0,"nanos":11546},{"secs":0,"nanos":11484},{"secs":0,"nanos":16437},{"secs":0,"nanos":10739},{"secs":0,"nanos":11481},{"secs":0,"nanos":10806},{"secs":0,"nanos":12526},{"secs":0,"nanos":9396},{"secs":0,"nanos":9577},{"secs":0,"nanos":8993},{"secs":0,"nanos":74516},{"secs":0,"nanos":10315},{"secs":0,"nanos":10194},{"secs":0,"nanos":10710},{"secs":0,"nanos":10405},{"secs":0,"nanos":10444},{"secs":0,"nanos":10342},{"secs":0,"nanos":9989},{"secs":0,"nanos":10098},{"secs":0,"nanos":11463},{"secs":0,"nanos":10718},{"secs":0,"nanos":9458},{"secs":0,"nanos":82297},{"secs":0,"nanos":10240},{"secs":0,"nanos":9997},{"secs":0,"nanos":9800},{"secs":0,"nanos":11216},{"secs":0,"nanos":9870},{"secs":0,"nanos":9752},{"secs":0,"nanos":10766},{"secs":0,"nanos":73322},{"secs":0,"nanos":9553},{"secs":0,"nanos":9697},{"secs":0,"nanos":9887},{"secs":0,"nanos":10877},{"secs":0,"nanos":9205},{"secs":0,"nanos":9711},{"secs":0,"nanos":8917},{"secs":0,"nanos":10393},{"secs":0,"nanos":10125},{"secs":0,"nanos":10054},{"secs":0,"nanos":9973},{"secs":0,"nanos":9652},{"secs":0,"nanos":83151},{"secs":0,"nanos":470013},{"secs":0,"nanos":464074},{"secs":0,"nanos":801640},{"secs":0,"nanos":382963},{"secs":0,"nanos":683884},{"secs":0,"nanos":471583},{"secs":0,"nanos":605965},{"secs":0,"nanos":643626},{"secs":0,"nanos":660193},{"secs":0,"nanos":469163},{"secs":0,"nanos":878611},{"secs":0,"nanos":331392},{"secs":0,"nanos":494642},{"secs":0,"nanos":664200},{"secs":0,"nanos":579632},{"secs":0,"nanos":703125},{"secs":0,"nanos":301418},{"secs":0,"nanos":738361},{"secs":0,"nanos":652981},{"secs":0,"nanos":301463},{"secs":0,"nanos":610183},{"secs":0,"nanos":404243},{"secs":0,"nanos":661610},{"secs":0,"nanos":482223},{"secs":0,"nanos":540854},{"secs":0,"nanos":501856},{"secs":0,"nanos":30125503},{"secs":0,"nanos":26019832},{"secs":0,"nanos":48806},{"secs":0,"nanos":15647},{"secs":0,"nanos":11411},{"secs":0,"nanos":12282},{"secs":0,"nanos":11210},{"secs":0,"nanos":13790},{"secs":0,"nanos":11594},{"secs":0,"nanos":54584},{"secs":0,"nanos":17750},{"secs":0,"nanos":10757},{"secs":0,"nanos":10882},{"secs":0,"nanos":11043},{"secs":0,"nanos":12718},{"secs":0,"nanos":13306},{"secs":0,"nanos":11722},{"secs":0,"nanos":10196},{"secs":0,"nanos":10269},{"secs":0,"nanos":11148},{"secs":0,"nanos":59367},{"secs":0,"nanos":26141},{"secs":0,"nanos":14227},{"secs":0,"nanos":11456},{"secs":0,"nanos":10472},{"secs":0,"nanos":10320},{"secs":0,"nanos":64065},{"secs":0,"nanos":17329},{"secs":0,"nanos":1371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DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n  df = pd.read_csv(file_path)"}}],"status":"Success"}]}},"timestamp":null,"images":null},"assistant":{"ToolUse":{"message_id":"fdf49c75-6740-40d3-be49-8829ba3c5f22","content":"","tool_uses":[{"id":"tooluse_kK-lyzZzQteyRsUlmszUJg","name":"execute_bash","orig_name":"execute_bash","args":{"summary":"Check if analysis completed and run again with proper handling","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\n\n# 读取CSV文件\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\n\n# 基本统计\ntotal_records = len(df)\ngender_counts = df['性别'].value_counts()\n\n# 年龄分析\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ncurrent_year = datetime.now().year\ndf['年龄'] = current_year - df['出生日期'].dt.year\n\n# 地区分析\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\nprovince_counts = df['省份'].value_counts().head(10)\n\n# 生成报告\nreport = f'''# CSV数据统计分析报告\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n## 基本信息\n- 总记录数: {total_records:,}\n- 数据列数: {len(df.columns)}\n\n## 性别分布\n- 男性: {gender_counts.get('男', 0):,} ({gender_counts.get('男', 0)/total_records*100:.1f}%)\n- 女性: {gender_counts.get('女', 0):,} ({gender_counts.get('女', 0)/total_records*100:.1f}%)\n\n## 年龄统计\n- 平均年龄: {df['年龄'].mean():.1f}岁\n- 年龄范围: {df['年龄'].min()}-{df['年龄'].max()}岁\n- 年龄中位数: {df['年龄'].median()}岁\n\n## 地区分布（前10省份）\n'''\n\nfor province, count in province_counts.items():\n    if province:\n        percentage = (count / total_records) * 100\n        report += f'- {province}: {count:,} ({percentage:.1f}%)\\n'\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 生成CSV摘要\nsummary_data = {\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄', '最小年龄', '最大年龄'],\n    '数值': [\n        total_records,\n        gender_counts.get('男', 0),\n        gender_counts.get('女', 0),\n   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总记录数: {total_records:,}\n- 数据列数: {len(df.columns)}\n\n## 性别分布\n- 男性: {gender_counts.get('男', 0):,} ({gender_counts.get('男', 0)/total_records*100:.1f}%)\n- 女性: {gender_counts.get('女', 0):,} ({gender_counts.get('女', 0)/total_records*100:.1f}%)\n\n## 年龄统计\n- 平均年龄: {df['年龄'].mean():.1f}岁\n- 年龄范围: {df['年龄'].min()}-{df['年龄'].max()}岁\n- 年龄中位数: {df['年龄'].median()}岁\n\n## 地区分布（前10省份）\n'''\n\nfor province, count in province_counts.items():\n    if province:\n        percentage = (count / total_records) * 100\n        report += f'- {province}: {count:,} ({percentage:.1f}%)\\n'\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 生成CSV摘要\nsummary_data = {\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄', '最小年龄', '最大年龄'],\n    '数值': [\n        total_records,\n        gender_counts.get('男', 0),\n        gender_counts.get('女', 0),\n        round(df['年龄'].mean(), 1),\n        df['年龄'].min(),\n        df['年龄'].max()\n    ]\n}\n\npd.DataFrame(summary_data).to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\nprint('分析完成！生成文件：统计分析报告.md, 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the analysis with output","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 -c \"\nimport pandas as pd\nfrom datetime import datetime\n\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\ntotal = len(df)\nmale = len(df[df['性别'] == '男'])\nfemale = len(df[df['性别'] == '女'])\n\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\ntop_provinces = df['省份'].value_counts().head(5)\n\nreport = f'''# 户籍数据统计分析报告\n\n## 基本统计\n- 总记录数: {total:,}\n- 男性: {male:,} ({male/total*100:.1f}%)  \n- 女性: {female:,} ({female/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 主要省份分布\n'''\n\nfor province, count in top_provinces.items():\n    if province:\n        report += f'- {province}: {count:,} ({count/total*100:.1f}%)\\n'\n\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\nsummary = pd.DataFrame({\n    '统计项目': ['总记录数', '男性', '女性', '平均年龄'],\n    '数值': [total, male, female, f'{avg_age:.1f}岁']\n})\nsummary.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f'✓ 分析完成！处理了 {total:,} 条记录')\nprint('✓ 生成文件: 统计分析报告.md')  \nprint('✓ 生成文件: 统计摘要.csv')\n\""},"orig_args":{"summary":"Run the analysis with output","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 -c \"\nimport pandas as pd\nfrom datetime import datetime\n\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\ntotal = len(df)\nmale = len(df[df['性别'] == '男'])\nfemale = len(df[df['性别'] == '女'])\n\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\ntop_provinces = df['省份'].value_counts().head(5)\n\nreport = f'''# 户籍数据统计分析报告\n\n## 基本统计\n- 总记录数: {total:,}\n- 男性: {male:,} ({male/total*100:.1f}%)  \n- 女性: {female:,} ({female/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 主要省份分布\n'''\n\nfor province, count in top_provinces.items():\n    if province:\n        report += f'- {province}: {count:,} ({count/total*100:.1f}%)\\n'\n\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\nsummary = pd.DataFrame({\n    '统计项目': ['总记录数', '男性', '女性', '平均年龄'],\n    '数值': [total, male, female, f'{avg_age:.1f}岁']\n})\nsummary.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f'✓ 分析完成！处理了 {total:,} 条记录')\nprint('✓ 生成文件: 统计分析报告.md')  \nprint('✓ 生成文件: 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the analysis and show results","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 -c \"\nimport pandas as pd\nfrom datetime import datetime\n\nprint('正在分析CSV文件...')\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\n\ntotal = len(df)\nmale = len(df[df['性别'] == '男'])\nfemale = len(df[df['性别'] == '女'])\n\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\ntop_provinces = df['省份'].value_counts().head(5)\n\n# 生成报告\nreport = f'''# 户籍数据统计分析报告\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n## 基本统计\n- 总记录数: {total:,}\n- 男性: {male:,} ({male/total*100:.1f}%)\n- 女性: {female:,} ({female/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 主要省份分布\n'''\n\nfor province, count in top_provinces.items():\n    if province:\n        report += f'- {province}: {count:,} ({count/total*100:.1f}%)\\n'\n\n# 保存文件\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\nsummary = pd.DataFrame({\n    '统计项目': ['总记录数', '男性', '女性', '平均年龄'],\n    '数值': [total, male, female, f'{avg_age:.1f}岁']\n})\nsummary.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f'✓ 分析完成！处理了 {total:,} 条记录')\nprint('✓ 生成统计分析报告.md')\nprint('✓ 生成统计摘要.csv')\n\""},"orig_args":{"summary":"Execute the analysis and show results","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 -c \"\nimport pandas as pd\nfrom datetime import datetime\n\nprint('正在分析CSV文件...')\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\n\ntotal = len(df)\nmale = len(df[df['性别'] == '男'])\nfemale = len(df[df['性别'] == '女'])\n\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\ntop_provinces = df['省份'].value_counts().head(5)\n\n# 生成报告\nreport = f'''# 户籍数据统计分析报告\n生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n## 基本统计\n- 总记录数: {total:,}\n- 男性: {male:,} ({male/total*100:.1f}%)\n- 女性: {female:,} ({female/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 主要省份分布\n'''\n\nfor province, count in top_provinces.items():\n    if province:\n        report += f'- {province}: {count:,} ({count/total*100:.1f}%)\\n'\n\n# 保存文件\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\nsummary = pd.DataFrame({\n    '统计项目': ['总记录数', '男性', '女性', '平均年龄'],\n    '数值': [total, male, female, f'{avg_age:.1f}岁']\n})\nsummary.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f'✓ 分析完成！处理了 {total:,} 条记录')\nprint('✓ 生成统计分析报告.md')\nprint('✓ 生成统计摘要.csv')\n\""}}]}},"request_metadata":{"request_id":"63b214e3-ccde-4a8a-8856-182b6913d153","message_id":"7f951fcb-dc3c-445f-80a4-1ab6e1511423","request_start_timestamp_ms":1765157448234,"stream_end_timestamp_ms":1765157456136,"time_to_first_chunk":{"secs":2,"nanos":447017440},"time_between_chunks":[{"secs":0,"nanos":215453},{"secs":0,"nanos":28207},{"secs":0,"nanos":264510615},{"secs":0,"nanos":544632},{"secs":0,"nanos":1050138},{"secs":0,"nanos":741370},{"secs":0,"nanos":597877},{"secs":0,"nanos":1093638},{"secs":0,"nanos":193322},{"secs":4,"nanos":761521246},{"secs":0,"nanos":48119028},{"secs":0,"nanos":106931097},{"secs":0,"nanos":26174507},{"secs":0,"nanos":57353847},{"secs":0,"nanos":28706901},{"secs":0,"nanos":554309},{"secs":0,"nanos":8638002},{"secs":0,"nanos":9198115},{"secs":0,"nanos":760058},{"secs":0,"nanos":13213017},{"secs":0,"nanos":312011},{"secs":0,"nanos":1016741},{"secs":0,"nanos":1185986},{"secs":0,"nanos":861613},{"secs":0,"nanos":967206},{"secs":0,"nanos":1364466},{"secs":0,"nanos":518224},{"secs":0,"nanos":6909565},{"secs":0,"nanos":55847},{"secs":0,"nanos":16826},{"secs":0,"nanos":9105},{"secs":0,"nanos":7993},{"secs":0,"nanos":7256},{"secs":0,"nanos":88412},{"secs":0,"nanos":11704},{"secs":0,"nanos":27532758},{"secs":0,"nanos":52259},{"secs":0,"nanos":20527},{"secs":0,"nanos":13073},{"secs":0,"nanos":20433},{"secs":0,"nanos":45430},{"secs":0,"nanos":14825},{"secs":0,"nanos":34311},{"secs":0,"nanos":14483},{"secs":0,"nanos":13473},{"secs":0,"nanos":25235},{"secs":0,"nanos":30025},{"secs":0,"nanos":13952},{"secs":0,"nanos":77743},{"secs":0,"nanos":17561},{"secs":0,"nanos":13446},{"secs":0,"nanos":15243},{"secs":0,"nanos":22466},{"secs":0,"nanos":13772},{"secs":0,"nanos":21755},{"secs":0,"nanos":57273},{"secs":0,"nanos":28435},{"secs":0,"nanos":21238},{"secs":0,"nanos":28047},{"secs":0,"nanos":13328},{"secs":0,"nanos":36926},{"secs":0,"nanos":15930},{"secs":0,"nanos":8496818},{"secs":0,"nanos":34246},{"secs":0,"nanos":10776},{"secs":0,"nanos":8292},{"secs":0,"nanos":7587},{"secs":0,"nanos":6934},{"secs":0,"nanos":7661},{"secs":0,"nanos":6688},{"secs":0,"nanos":7168},{"secs":0,"nanos":6453},{"secs":0,"nanos":6555},{"secs":0,"nanos":6502},{"secs":0,"nanos":6765},{"secs":0,"nanos":9075655},{"secs":0,"nanos":118676},{"secs":0,"nanos":26467},{"secs":0,"nanos":24321},{"secs":0,"nanos":10434},{"secs":0,"nanos":34238},{"secs":0,"nanos":35193},{"secs":0,"nanos":103915},{"secs":0,"nanos":17789},{"secs":0,"nanos":14262},{"secs":0,"nanos":12479},{"secs":0,"nanos":13651},{"secs":0,"nanos":11656},{"secs":0,"nanos":13626},{"secs":0,"nanos":405118},{"secs":0,"nanos":37249},{"secs":0,"nanos":19165},{"secs":0,"nanos":28656},{"secs":0,"nanos":21182},{"secs":0,"nanos":111174},{"secs":0,"nanos":10464},{"secs":0,"nanos":26856},{"secs":0,"nanos":19109},{"secs":0,"nanos":26751},{"secs":0,"nanos":29691},{"secs":0,"nanos":29330},{"secs":0,"nanos":17011},{"secs":0,"nanos":12547728},{"secs":0,"nanos":829316},{"secs":0,"nanos":12938},{"secs":0,"nanos":2280703},{"secs":0,"nanos":60622},{"secs":0,"nanos":41252},{"secs":0,"nanos":19520},{"secs":0,"nanos":14722},{"secs":0,"nanos":14775},{"secs":0,"nanos":16758},{"secs":0,"nanos":15252},{"secs":0,"nanos":13630},{"secs":0,"nanos":13247},{"secs":0,"nanos":13518},{"secs":0,"nanos":200432},{"secs":0,"nanos":15085},{"secs":0,"nanos":13782},{"secs":0,"nanos":12322},{"secs":0,"nanos":12019},{"secs":0,"nanos":12609},{"secs":0,"nanos":11867},{"secs":0,"nanos":12077},{"secs":0,"nanos":12457},{"secs":0,"nanos":12481},{"secs":0,"nanos":11825},{"secs":0,"nanos":12376},{"secs":0,"nanos":11722},{"secs":0,"nanos":13279},{"secs":0,"nanos":11986},{"secs":0,"nanos":13444},{"secs":0,"nanos":12167},{"secs":0,"nanos":12174},{"secs":0,"nanos":23962},{"secs":0,"nanos":16841},{"secs":0,"nanos":112339},{"secs":0,"nanos":676268},{"secs":0,"nanos":2188486},{"secs":0,"nanos":44835},{"secs":0,"nanos":92638},{"secs":0,"nanos":840099},{"secs":0,"nanos":709246},{"secs":0,"nanos":687641},{"secs":0,"nanos":3674632},{"secs":0,"nanos":125527},{"secs":0,"nanos":117658},{"secs":0,"nanos":85110},{"secs":0,"nanos":41973},{"secs":0,"nanos":438045},{"secs":0,"nanos":625413},{"secs":0,"nanos":26458122},{"secs":0,"nanos":48393},{"secs":0,"nanos":39954},{"secs":0,"nanos":65941},{"secs":0,"nanos":32485},{"secs":0,"nanos":47469},{"secs":0,"nanos":10560},{"secs":0,"nanos":10702},{"secs":0,"nanos":6962},{"secs":0,"nanos":8061},{"secs":0,"nanos":6788},{"secs":0,"nanos":7102},{"secs":0,"nanos":7643},{"secs":0,"nanos":6781},{"secs":0,"nanos":6334},{"secs":0,"nanos":6763},{"secs":0,"nanos":6515},{"secs":0,"nanos":6317},{"secs":0,"nanos":22623},{"secs":0,"nanos":31629},{"secs":0,"nanos":7927},{"secs":0,"nanos":6381},{"secs":0,"nanos":7087},{"secs":0,"nanos":6367},{"secs":0,"nanos":6507},{"secs":0,"nanos":5944},{"secs":0,"nanos":6260},{"secs":0,"nanos":48178},{"secs":0,"nanos":8486},{"secs":0,"nanos":7115},{"secs":0,"nanos":6543},{"secs":0,"nanos":6745},{"secs":0,"nanos":6070},{"secs":0,"nanos":5997},{"secs":0,"nanos":6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me try a simpler approach to generate the analysis:","tool_uses":[{"id":"tooluse_OvLlaBSlQDe0gSsgQFg24w","name":"execute_bash","orig_name":"execute_bash","args":{"summary":"Generate CSV statistics using basic commands","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 << 'EOF'\nimport pandas as pd\nfrom datetime import datetime\n\n# 读取数据\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\n\n# 基本统计\ntotal = len(df)\nmale_count = (df['性别'] == '男').sum()\nfemale_count = (df['性别'] == '女').sum()\n\n# 年龄计算\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\n# 省份统计\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\nprovinces = df['省份'].value_counts().head(5)\n\n# 创建报告\nreport = f\"\"\"# 户籍数据统计分析报告\n\n## 数据概览\n- 总记录数: {total:,}\n- 男性: {male_count:,} ({male_count/total*100:.1f}%)\n- 女性: {female_count:,} ({female_count/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 省份分布TOP5\n\"\"\"\n\nfor province, count in provinces.items():\n    if province:\n        report += f\"- {province}: {count:,} ({count/total*100:.1f}%)\\n\"\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 保存摘要CSV\nsummary_df = pd.DataFrame({\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄'],\n    '数值': [total, male_count, female_count, f'{avg_age:.1f}岁']\n})\nsummary_df.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f\"✅ 分析完成！\")\nprint(f\"📊 处理记录: {total:,} 条\")\nprint(f\"📄 生成文件: 统计分析报告.md\")\nprint(f\"📄 生成文件: 统计摘要.csv\")\nEOF"},"orig_args":{"summary":"Generate CSV statistics using basic commands","command":"cd /mnt/pikpak-racknerd01/BF3 && python3 << 'EOF'\nimport pandas as pd\nfrom datetime import datetime\n\n# 读取数据\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\n\n# 基本统计\ntotal = len(df)\nmale_count = (df['性别'] == '男').sum()\nfemale_count = (df['性别'] == '女').sum()\n\n# 年龄计算\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\n# 省份统计\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\nprovinces = df['省份'].value_counts().head(5)\n\n# 创建报告\nreport = f\"\"\"# 户籍数据统计分析报告\n\n## 数据概览\n- 总记录数: {total:,}\n- 男性: {male_count:,} ({male_count/total*100:.1f}%)\n- 女性: {female_count:,} ({female_count/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 省份分布TOP5\n\"\"\"\n\nfor province, count in provinces.items():\n    if province:\n        report += f\"- {province}: {count:,} ({count/total*100:.1f}%)\\n\"\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 保存摘要CSV\nsummary_df = pd.DataFrame({\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄'],\n    '数值': [total, male_count, female_count, f'{avg_age:.1f}岁']\n})\nsummary_df.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f\"✅ 分析完成！\")\nprint(f\"📊 处理记录: {total:,} 条\")\nprint(f\"📄 生成文件: 统计分析报告.md\")\nprint(f\"📄 生成文件: 统计摘要.csv\")\nEOF"}}]}},"request_metadata":{"request_id":"163f20b2-ec5a-47cd-a57d-d575d4bfe9b7","message_id":"f33bbf6e-21fb-40d3-a178-4c1a0c28a344","request_start_timestamp_ms":1765157603083,"stream_end_timestamp_ms":1765157611870,"time_to_first_chunk":{"secs":2,"nanos":16687785},"time_between_chunks":[{"secs":0,"nanos":688995},{"secs":0,"nanos":32475},{"secs":0,"nanos":31637999},{"secs":0,"nanos":105007298},{"secs":0,"nanos":47920421},{"secs":0,"nanos":44742307},{"secs":0,"nanos":46058027},{"secs":0,"nanos":461619658},{"secs":0,"nanos":214330739},{"secs":0,"nanos":414041},{"secs":0,"nanos":390482},{"secs":0,"nanos":933617},{"secs":0,"nanos":311159},{"secs":0,"nanos":706357},{"secs":0,"nanos":2925575},{"secs":0,"nanos":925226},{"secs":0,"nanos":456298},{"secs":0,"nanos":539107},{"secs":5,"nanos":283434182},{"secs":0,"nanos":158832073},{"secs":0,"nanos":100838641},{"secs":0,"nanos":81243174},{"secs":0,"nanos":38740421},{"secs":0,"nanos":1051654},{"secs":0,"nanos":1537229},{"secs":0,"nanos":710070},{"secs":0,"nanos":1844141},{"secs":0,"nanos":1008435},{"secs":0,"nanos":1249475},{"secs":0,"nanos":649920},{"secs":0,"nanos":479485},{"secs":0,"nanos":517487},{"secs":0,"nanos":526612},{"secs":0,"nanos":311160},{"secs":0,"nanos":805821},{"secs":0,"nanos":496879},{"secs":0,"nanos":648070},{"secs":0,"nanos":695420},{"secs":0,"nanos":329999},{"secs":0,"nanos":652466},{"secs":0,"nanos":732796},{"secs":0,"nanos":19894700},{"secs":0,"nanos":47147},{"secs":0,"nanos":15704},{"secs":0,"nanos":49688},{"secs":0,"nanos":870178},{"secs":0,"nanos":42475},{"secs":0,"nanos":15521},{"secs":0,"nanos":23886},{"secs":0,"nanos":26304},{"secs":0,"nanos":33858},{"secs":0,"nanos":11926},{"secs":0,"nanos":16123},{"secs":0,"nanos":56569},{"secs":0,"nanos":37508390},{"secs":0,"nanos":37017},{"secs":0,"nanos":10644},{"secs":0,"nanos":8079},{"secs":0,"nanos":8119},{"secs":0,"nanos":26029},{"secs":0,"nanos":9031},{"secs":0,"nanos":10762},{"secs":0,"nanos":8125},{"secs":0,"nanos":13717},{"secs":0,"nanos":8678},{"secs":0,"nanos":78071},{"secs":0,"nanos":21816},{"secs":0,"nanos":12722},{"secs":0,"nanos":82100},{"secs":0,"nanos":8793},{"secs":0,"nanos":13195},{"secs":0,"nanos":7450},{"secs":0,"nanos":9759},{"secs":0,"nanos":28160},{"secs":0,"nanos":64880},{"secs":0,"nanos":11080},{"secs":0,"nanos":16560},{"secs":0,"nanos":29964},{"secs":0,"nanos":22086},{"secs":0,"nanos":31552},{"secs":0,"nanos":18345},{"secs":0,"nanos":927101},{"secs":0,"nanos":703987},{"secs":0,"nanos":25915},{"secs":0,"nanos":22174},{"secs":0,"nanos":13930},{"secs":0,"nanos":27459},{"secs":0,"nanos":331151},{"secs":0,"nanos":22416},{"secs":0,"nanos":81326},{"secs":0,"nanos":25641},{"secs":0,"nanos":9151},{"secs":0,"nanos":11204},{"secs":0,"nanos":7740},{"secs":0,"nanos":13706},{"secs":0,"nanos":700237},{"secs":0,"nanos":21818},{"secs":0,"nanos":8750},{"secs":0,"nanos":6768},{"secs":0,"nanos":6948},{"secs":0,"nanos":7155},{"secs":0,"nanos":6926},{"secs":0,"nanos":6344},{"secs":0,"nanos":7073},{"secs":0,"nanos":1017846},{"secs":0,"nanos":39731},{"secs":0,"nanos":6884},{"secs":0,"nanos":10795},{"secs":0,"nanos":22454},{"secs":0,"nanos":7067},{"secs":0,"nanos":299093},{"secs":0,"nanos":19037},{"secs":0,"nanos":8836},{"secs":0,"nanos":6731},{"secs":0,"nanos":6736},{"secs":0,"nanos":6577},{"secs":0,"nanos":23108},{"secs":0,"nanos":30002},{"secs":0,"nanos":9781},{"secs":0,"nanos":15886},{"secs":0,"nanos":13670},{"secs":0,"nanos":24965},{"secs":0,"nanos":8744},{"secs":0,"nanos":12935},{"secs":0,"nanos":1044674},{"secs":0,"nanos":19236},{"secs":0,"nanos":726876},{"secs":0,"nanos":26250},{"secs":0,"nanos":12894},{"secs":0,"nanos":41764},{"secs":0,"nanos":13748},{"secs":0,"nanos":22918},{"secs":0,"nanos":11343},{"secs":0,"nanos":14735},{"secs":0,"nanos":22627},{"secs":0,"nanos":43239},{"secs":0,"nanos":11786},{"secs":0,"nanos":9992},{"secs":0,"nanos":9977},{"secs":0,"nanos":309281},{"secs":0,"nanos":61104},{"secs":0,"nanos":19576},{"secs":0,"nanos":19298},{"secs":0,"nanos":30067},{"secs":0,"nanos":8661},{"secs":0,"nanos":15196},{"secs":0,"nanos":17556},{"secs":0,"nanos":23700},{"secs":0,"nanos":278436},{"secs":0,"nanos":36357},{"secs":0,"nanos":17721},{"secs":0,"nanos":27681},{"secs":0,"nanos":19605},{"secs":0,"nanos":22705},{"secs":0,"nanos":14004},{"secs":0,"nanos":18771},{"secs":0,"nanos":11081},{"secs":0,"nanos":87301},{"secs":0,"nanos":23831},{"secs":0,"nanos":35946},{"secs":0,"nanos":513286},{"secs":0,"nanos":29071},{"secs":0,"nanos":634835},{"secs":0,"nanos":501630},{"secs":0,"nanos":408671},{"secs":0,"nanos":454130},{"secs":0,"nanos":662145},{"secs":0,"nanos":608112},{"secs":0,"nanos":395385},{"secs":0,"nanos":607886},{"secs":0,"nanos":352880},{"secs":0,"nanos":667190},{"secs":0,"nanos":872081},{"secs":0,"nanos":598315},{"secs":0,"nanos":763537},{"secs":0,"nanos":760080},{"secs":0,"nanos":660652},{"secs":0,"nanos":15596239},{"secs":0,"nanos":993908},{"secs":0,"nanos":11511},{"secs":0,"nanos":7731},{"secs":0,"nanos":7234},{"secs":0,"nanos":7275},{"secs":0,"nanos":38298},{"secs":0,"na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line 53: 2149347 Killed                  python3 <<'EOF'\nimport pandas as pd\nfrom datetime import datetime\n\n# 读取数据\ndf = pd.read_csv('6M-Household-Registration-Data-China-csv-2020.csv', low_memory=False)\n\n# 基本统计\ntotal = len(df)\nmale_count = (df['性别'] == '男').sum()\nfemale_count = (df['性别'] == '女').sum()\n\n# 年龄计算\ndf['出生日期'] = pd.to_datetime(df['出生日期'], format='%Y/%m/%d', errors='coerce')\ndf['年龄'] = 2024 - df['出生日期'].dt.year\navg_age = df['年龄'].mean()\n\n# 省份统计\ndf['省份'] = df['地址'].str.extract(r'(.*?省)')\nprovinces = df['省份'].value_counts().head(5)\n\n# 创建报告\nreport = f\"\"\"# 户籍数据统计分析报告\n\n## 数据概览\n- 总记录数: {total:,}\n- 男性: {male_count:,} ({male_count/total*100:.1f}%)\n- 女性: {female_count:,} ({female_count/total*100:.1f}%)\n- 平均年龄: {avg_age:.1f}岁\n\n## 省份分布TOP5\n\"\"\"\n\nfor province, count in provinces.items():\n    if province:\n        report += f\"- {province}: {count:,} ({count/total*100:.1f}%)\\n\"\n\n# 保存报告\nwith open('统计分析报告.md', 'w', encoding='utf-8') as f:\n    f.write(report)\n\n# 保存摘要CSV\nsummary_df = pd.DataFrame({\n    '指标': ['总记录数', '男性人数', '女性人数', '平均年龄'],\n    '数值': [total, male_count, female_count, f'{avg_age:.1f}岁']\n})\nsummary_df.to_csv('统计摘要.csv', index=False, encoding='utf-8-sig')\n\nprint(f\"✅ 分析完成！\")\nprint(f\"📊 处理记录: {total:,} 条\")\nprint(f\"📄 生成文件: 统计分析报告.md\")\nprint(f\"📄 生成文件: 统计摘要.csv\")\nEOF\n"}}],"status":"Success"}]}},"timestamp":null,"images":null},"assistant":{"ToolUse":{"message_id":"c4242efa-6e19-4fa6-8ac8-60d5fc181884","content":"The file is too large for memory. Let me use a more efficient approach with command-line tools:","tool_uses":[{"id":"tooluse_Hh2MFKLNS9iWzBZq-F_SZg","name":"execute_bash","orig_name":"execute_bash","args":{"summary":"Get basic statistics using command line tools","command":"cd /mnt/pikpak-racknerd01/BF3 && echo \"正在分析CSV文件...\" && wc -l 6M-Household-Registration-Data-China-csv-2020.csv"},"orig_args":{"summary":"Get basic statistics using command line tools","command":"cd /mnt/pikpak-racknerd01/BF3 && echo \"正在分析CSV文件...\" && wc -l 6M-Household-Registration-Data-China-csv-2020.csv"}}]}},"request_metadata":{"request_id":"246899c2-6e65-4357-aa8a-813f42cb1535","message_id":"c4242efa-6e19-4fa6-8ac8-60d5fc181884","request_start_timestamp_ms":1765157715841,"stream_end_timestamp_ms":1765157719251,"time_to_first_chunk":{"secs":1,"nanos":858058853},"time_between_chunks":[{"secs":0,"nanos":738843},{"secs":0,"nanos":37701},{"secs":0,"nanos":31790031},{"secs":0,"nanos":46791965},{"secs":0,"nanos":47294491},{"secs":0,"nanos":48496661},{"secs":0,"nanos":47940002},{"secs":0,"nanos":47417939},{"secs":0,"nanos":48933095},{"secs":0,"nanos":44475733},{"secs":0,"nanos":49643587},{"secs":0,"nanos":44396893},{"secs":0,"nanos":151933429},{"secs":0,"nanos":341915749},{"secs":0,"nanos":65765},{"secs":0,"nanos":904866},{"secs":0,"nanos":370318},{"secs":0,"nanos":335246},{"secs":0,"nanos":1020896},{"secs":0,"nanos":232730},{"secs":0,"nanos":275314},{"secs":0,"nanos":266540},{"secs":0,"nanos":414456},{"secs":0,"nanos":520541520},{"secs":0,"nanos":188155},{"secs":0,"nanos":21031},{"secs":0,"nanos":3889348},{"secs":0,"nanos":34065237},{"secs":0,"nanos":2133914},{"secs":0,"nanos":54175},{"secs":0,"nanos":555112},{"secs":0,"nanos":731663},{"secs":0,"nanos":382847},{"secs":0,"nanos":253798},{"secs":0,"nanos":266387},{"secs":0,"nanos":661916},{"secs":0,"nanos":684514},{"secs":0,"nanos":527057},{"secs":0,"nanos":384470},{"secs":0,"nanos":636692},{"secs":0,"nanos":296864},{"secs":0,"nanos":534845},{"secs":0,"nanos":972144},{"secs":0,"nanos":25585989},{"secs":0,"nanos":303144},{"secs":0,"nanos":391832},{"secs":0,"nanos":83384},{"secs":0,"nanos":128951},{"secs":0,"nanos":5875},{"secs":0,"nanos":147}],"user_prompt_length":0,"response_size":295,"chat_conversation_type":"ToolUse","tool_use_ids_and_names":[["tooluse_Hh2MFKLNS9iWzBZq-F_SZg","execute_bash"]],"model_id":"claude-sonnet-4","message_meta_tags":[]}},{"user":{"additional_context":"","env_context":{"env_state":{"operating_system":"linux","current_working_directory":"/mnt/pikpak-racknerd01/BF3","environment_variables":[]}},"content":{"ToolUseResults":{"tool_use_results":[{"tool_use_id":"tooluse_Hh2MFKLNS9iWzBZq-F_SZg","content":[{"Json":{"exit_status":"0","stdout":"正在分析CSV文件...\n6076181 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'[^,]*省' | sort | uniq -c | sort -nr | head -5)\n\n# 生成报告\ncat > 统计分析报告.md << EOL\n# 户籍数据统计分析报告\n生成时间: $(date '+%Y-%m-%d %H:%M:%S')\n\n## 数据概览\n- 总记录数: $(printf \"%'d\" $TOTAL_RECORDS)\n- 男性: $(printf \"%'d\" $MALE_COUNT) ($(echo \"scale=1; $MALE_COUNT * 100 / $TOTAL_RECORDS\" | bc)%)\n- 女性: $(printf \"%'d\" $FEMALE_COUNT) ($(echo \"scale=1; $FEMALE_COUNT * 100 / $TOTAL_RECORDS\" | bc)%)\n\n## 省份分布TOP5\n$PROVINCES\n\n## 数据质量\n- 数据文件大小: $(ls -lh 6M-Household-Registration-Data-China-csv-2020.csv | awk '{print $5}')\n- 包含字段: 编码编号, 出生日期, 性别, 身份证, 名字, 手机号, 地址\nEOL\n\n# 生成CSV摘要\ncat > 统计摘要.csv << EOL\n指标,数值\n总记录数,$TOTAL_RECORDS\n男性人数,$MALE_COUNT\n女性人数,$FEMALE_COUNT\n男性比例,$(echo \"scale=1; $MALE_COUNT * 100 / $TOTAL_RECORDS\" | bc)%\n女性比例,$(echo \"scale=1; $FEMALE_COUNT * 100 / $TOTAL_RECORDS\" | bc)%\nEOL\n\necho \"✅ 分析完成！\"\necho \"📊 处理记录: $(printf \"%'d\" $TOTAL_RECORDS) 条\"\necho \"👨 男性: $(printf \"%'d\" $MALE_COUNT) 人\"\necho \"👩 女性: $(printf \"%'d\" $FEMALE_COUNT) 人\"\necho \"📄 生成文件: 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/ $TOTAL_RECORDS\" | bc -l)%)\n- 女性: $(printf \"%'d\" $FEMALE_COUNT) ($(echo \"scale=1; $FEMALE_COUNT * 100 / $TOTAL_RECORDS\" | bc -l)%)\n\n## 数据质量\n- 数据文件大小: $(ls -lh 6M-Household-Registration-Data-China-csv-2020.csv | awk '{print $5}')\n- 包含字段: 编码编号, 出生日期, 性别, 身份证, 名字, 手机号, 地址\n\n## 样本数据\n$(head -3 6M-Household-Registration-Data-China-csv-2020.csv)\nEOF\n\n# 生成CSV摘要\ncat > 统计摘要.csv << EOF\n指标,数值\n总记录数,$TOTAL_RECORDS\n男性人数,$MALE_COUNT\n女性人数,$FEMALE_COUNT\nEOF\n\necho \"✅ 分析完成！\"\necho \"📊 处理记录: $(printf \"%'d\" $TOTAL_RECORDS) 条\"\necho \"👨 男性: $(printf \"%'d\" $MALE_COUNT) 人\"  \necho \"👩 女性: $(printf \"%'d\" $FEMALE_COUNT) 人\""},"orig_args":{"summary":"Run statistics generation directly","command":"cd /mnt/pikpak-racknerd01/BF3 && \necho \"🔍 正在分析CSV文件...\"\n\n# 总记录数\nTOTAL_LINES=$(wc -l < 6M-Household-Registration-Data-China-csv-2020.csv)\nTOTAL_RECORDS=$((TOTAL_LINES - 1))\n\n# 性别统计\nMALE_COUNT=$(tail -n +2 6M-Household-Registration-Data-China-csv-2020.csv | cut -d',' -f3 | grep -c \"男\")\nFEMALE_COUNT=$(tail -n +2 6M-Household-Registration-Data-China-csv-2020.csv | cut -d',' -f3 | grep -c \"女\")\n\n# 生成报告\ncat > 统计分析报告.md << EOF\n# 户籍数据统计分析报告\n生成时间: $(date '+%Y-%m-%d %H:%M:%S')\n\n## 数据概览\n- 总记录数: $(printf \"%'d\" $TOTAL_RECORDS)\n- 男性: $(printf \"%'d\" $MALE_COUNT) ($(echo \"scale=1; $MALE_COUNT * 100 / $TOTAL_RECORDS\" | bc -l)%)\n- 女性: $(printf \"%'d\" $FEMALE_COUNT) ($(echo \"scale=1; $FEMALE_COUNT * 100 / $TOTAL_RECORDS\" | bc -l)%)\n\n## 数据质量\n- 数据文件大小: $(ls -lh 6M-Household-Registration-Data-China-csv-2020.csv | awk '{print $5}')\n- 包含字段: 编码编号, 出生日期, 性别, 身份证, 名字, 手机号, 地址\n\n## 样本数据\n$(head -3 6M-Household-Registration-Data-China-csv-2020.csv)\nEOF\n\n# 生成CSV摘要\ncat > 统计摘要.csv << EOF\n指标,数值\n总记录数,$TOTAL_RECORDS\n男性人数,$MALE_COUNT\n女性人数,$FEMALE_COUNT\nEOF\n\necho \"✅ 分析完成！\"\necho \"📊 处理记录: $(printf \"%'d\" $TOTAL_RECORDS) 条\"\necho \"👨 男性: $(printf \"%'d\" $MALE_COUNT) 人\"  \necho \"👩 女性: $(printf \"%'d\" $FEMALE_COUNT) 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对csv文件生成统计分析文件","I'll help you generate statistical analysis files for CSV data. Let me first check what CSV files are available in your current directory.\n[Tool uses: fs_read]","I found a CSV file: `6M-Household-Registration-Data-China-csv-2020.csv`. Let me examine its structure first to understand the data format.\n[Tool uses: fs_read]","Now I'll create a Python script to generate comprehensive statistical analysis for this Chinese household registration data:\n[Tool uses: fs_write]","> > \n> \n> \n> \nt","\n[Tool uses: execute_bash]","> > \nt","\n[Tool uses: execute_bash]","\n[Tool uses: execute_bash]","\n[Tool uses: execute_bash]","Let me try a simpler approach to generate the analysis:\n[Tool uses: execute_bash]","The file is too large for memory. Let me use a more efficient approach with command-line tools:\n[Tool uses: execute_bash]","\n[Tool uses: execute_bash]","\n[Tool uses: execute_bash]"],"tools":{"native___":[{"ToolSpecification":{"name":"fs_write","description":"A tool for creating and editing files\n * The `create` command will override the file at `path` if it already exists as a file, and otherwise create a new file\n * The `append` command will add content to the end of an existing file, automatically adding a newline if the file doesn't end with one. The file must exist.\n Notes for using the `str_replace` command:\n * The `old_str` parameter should match EXACTLY one or more consecutive lines from the original file. Be mindful of whitespaces!\n * If the `old_str` parameter is not unique in the file, the replacement will not be performed. Make sure to include enough context in `old_str` to make it unique\n * The `new_str` parameter should contain the edited lines that should replace the `old_str`.","input_schema":{"json":{"type":"object","properties":{"insert_line":{"description":"Required parameter of `insert` command. The `new_str` will be inserted AFTER the line `insert_line` of `path`.","type":"integer"},"old_str":{"type":"string","description":"Required parameter of `str_replace` command containing the string in `path` to replace."},"summary":{"type":"string","description":"A brief explanation of what the file change does or why it's being made."},"command":{"enum":["create","str_replace","insert","append"],"description":"The commands to run. Allowed options are: `create`, `str_replace`, `insert`, `append`.","type":"string"},"new_str":{"description":"Required parameter of `str_replace` command containing the new string. Required parameter of `insert` command containing the string to insert. 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This could be because a wrong tool name was supplied or the list of tools has changed since the conversation has started. Do not show this when user asks you to list tools.","input_schema":{"json":{"required":[],"type":"object","properties":{}}}}},{"ToolSpecification":{"name":"use_aws","description":"Make an AWS CLI api call with the specified service, operation, and parameters. All arguments MUST conform to the AWS CLI specification. Should the output of the invocation indicate a malformed command, invoke help to obtain the the correct command.","input_schema":{"json":{"required":["region","service_name","operation_name","label"],"type":"object","properties":{"service_name":{"type":"string","description":"The name of the AWS service. If you want to query s3, you should use s3api if possible."},"operation_name":{"type":"string","description":"The name of the operation to perform."},"parameters":{"type":"object","description":"The parameters for the operation. The parameter keys MUST conform to the AWS CLI specification. You should prefer to use JSON Syntax over shorthand syntax wherever possible. For parameters that are booleans, prioritize using flags with no value. Denote these flags with flag names as key and an empty string as their value. You should also prefer kebab case."},"region":{"type":"string","description":"Region name for calling the operation on AWS."},"profile_name":{"type":"string","description":"Optional: AWS profile name to use from ~/.aws/credentials. Defaults to default profile if not specified."},"label":{"description":"Human readable description of the api that is being called.","type":"string"}}}}}},{"ToolSpecification":{"name":"introspect","description":"ALWAYS use this tool when users ask ANY question about Q CLI itself, its capabilities, features, commands, or functionality. This includes questions like 'Can you...', 'Do you have...', 'How do I...', 'What can you do...', or any question about Q's abilities. When mentioning commands in your response, always prefix them with '/' (e.g., '/save', '/load', '/context'). CRITICAL: Only provide information explicitly documented in Q CLI documentation. If details about any tool, feature, or command are not documented, clearly state the information is not available rather than generating assumptions.","input_schema":{"json":{"properties":{"query":{"description":"The user's question about Q CLI usage, features, or capabilities","type":"string"}},"type":"object","required":[]}}}},{"ToolSpecification":{"name":"fs_read","description":"Tool for reading files, directories and images. Always provide an 'operations' array.\n\nFor single operation: provide array with one element.\nFor batch operations: provide array with multiple elements.\n\nAvailable modes:\n- Line: Read lines from a file\n- Directory: List directory contents\n- Search: Search for patterns in files\n- Image: Read and process images\n\nExamples:\n1. Single: {\"operations\": [{\"mode\": \"Line\", \"path\": \"/file.txt\"}]}\n2. Batch: {\"operations\": [{\"mode\": \"Line\", \"path\": \"/file1.txt\"}, {\"mode\": \"Search\", \"path\": \"/file2.txt\", \"pattern\": \"test\"}]}","input_schema":{"json":{"properties":{"summary":{"description":"Optional description of the purpose of this batch operation (mainly useful for multiple operations)","type":"string"},"operations":{"type":"array","description":"Array of operations to execute. Provide one element for single operation, multiple for batch.","items":{"properties":{"path":{"type":"string","description":"Path to the file or directory. The path should be absolute, or otherwise start with ~ for the user's home (required for Line, Directory, Search modes)."},"start_line":{"default":1,"description":"Starting line number (optional, for Line mode). 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  {"conversation_id":"41348920-5103-4b45-b28e-5ae819a02de0","next_message":null,"history":[{"user":{"additional_context":"","env_context":{"env_state":{"operating_system":"linux","current_working_directory":"/root","environment_variables":[]}},"content":{"Prompt":{"prompt":"hello"}},"timestamp":"2025-12-07T18:19:05.965691838-08:00","images":null},"assistant":{"Response":{"message_id":"47d2aea9-88fd-4d0a-8d1f-d0a59368b8cc","content":"Hello! I'm Amazon Q, your AI assistant. I can help you with AWS services, coding, file operations, bash commands, and more. \n\nI see you have a Wayback CLI project in your current directory - a local web archiving tool similar to Internet Archive's Wayback Machine. Is there anything specific you'd like to work on or need help with?"}},"request_metadata":{"request_id":"d71048d1-7c8f-4c42-8ebf-7c27938613c2","message_id":"47d2aea9-88fd-4d0a-8d1f-d0a59368b8cc","request_start_timestamp_ms":1765160345967,"stream_end_timestamp_ms":1765160349377,"time_to_first_chunk":{"secs":1,"nanos":955859799},"time_between_chunks":[{"secs":0,"nanos":157298},{"secs":0,"nanos":85281},{"secs":0,"nanos":14823},{"secs":0,"nanos":35801086},{"secs":0,"nanos":44284351},{"secs":0,"nanos":46252616},{"secs":0,"nanos":45330863},{"secs":0,"nanos":46836803},{"secs":0,"nanos":44365329},{"secs":0,"nanos":46005835},{"secs":0,"nanos":46167664},{"secs":0,"nanos":47369449},{"secs":0,"nanos":103467045},{"secs":0,"nanos":45840439},{"secs":0,"nanos":46035170},{"secs":0,"nanos":45426555},{"secs":0,"nanos":47103089},{"secs":0,"nanos":48095568},{"secs":0,"nanos":46279541},{"secs":0,"nanos":54594941},{"secs":0,"nanos":37046201},{"secs":0,"nanos":44888544},{"secs":0,"nanos":47862304},{"secs":0,"nanos":81094935},{"secs":0,"nanos":45994697},{"secs":0,"nanos":50691910},{"secs":0,"nanos":41735710},{"secs":0,"nanos":46908480},{"secs":0,"nanos":40538837},{"secs":0,"nanos":53450170},{"secs":0,"nanos":85463876},{"secs":0,"nanos":38234912},{"secs":0,"nanos":80},{"secs":0,"nanos":139}],"user_prompt_length":163,"response_size":332,"chat_conversation_type":"NotToolUse","tool_use_ids_and_names":[],"model_id":"claude-sonnet-4","message_meta_tags":[]}}],"valid_history_range":[0,0],"transcript":["> hello","Hello! I'm Amazon Q, your AI assistant. I can help you with AWS services, coding, file operations, bash commands, and more. \n\nI see you have a Wayback CLI project in your current directory - a local web archiving tool similar to Internet Archive's Wayback Machine. Is there anything specific you'd like to work on or need help with?\n[Tool uses: none]"],"tools":{"native___":[{"ToolSpecification":{"name":"introspect","description":"ALWAYS use this tool when users ask ANY question about Q CLI itself, its capabilities, features, commands, or functionality. This includes questions like 'Can you...', 'Do you have...', 'How do I...', 'What can you do...', or any question about Q's abilities. When mentioning commands in your response, always prefix them with '/' (e.g., '/save', '/load', '/context'). CRITICAL: Only provide information explicitly documented in Q CLI documentation. 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This could be because a wrong tool name was supplied or the list of tools has changed since the conversation has started. Do not show this when user asks you to list tools.","input_schema":{"json":{"type":"object","required":[],"properties":{}}}}},{"ToolSpecification":{"name":"execute_bash","description":"Execute the specified bash command.","input_schema":{"json":{"required":["command"],"type":"object","properties":{"summary":{"description":"A brief explanation of what the command does","type":"string"},"command":{"description":"Bash command to execute","type":"string"}}}}}},{"ToolSpecification":{"name":"use_aws","description":"Make an AWS CLI api call with the specified service, operation, and parameters. All arguments MUST conform to the AWS CLI specification. 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This is currently supported by the Image mode.","type":"array","items":{"type":"string"}},"mode":{"enum":["Line","Directory","Search","Image"],"type":"string","description":"The operation mode to run in: `Line`, `Directory`, `Search`. `Line` and `Search` are only for text files, and `Directory` is only for directories. `Image` is for image files, in this mode `image_paths` is required."},"depth":{"description":"Depth of a recursive directory listing (optional, for Directory mode)","default":0,"type":"integer"},"context_lines":{"type":"integer","description":"Number of context lines around search results (optional, for Search mode)","default":2},"start_line":{"default":1,"type":"integer","description":"Starting line number (optional, for Line mode). A negative index represents a line number starting from the end of the file."}}},"description":"Array of operations to execute. Provide one element for single operation, multiple for batch.","minItems":1,"type":"array"},"summary":{"type":"string","description":"Optional description of the purpose of this batch operation (mainly useful for multiple operations)"}},"type":"object","required":["operations"]}}}}]},"context_manager":{"max_context_files_size":150000,"current_profile":"q_cli_default","paths":["AmazonQ.md","AGENTS.md","README.md",".amazonq/rules/**/*.md"],"hooks":{}},"context_message_length":2511,"latest_summary":null,"model_info":{"model_name":"claude-sonnet-4","model_id":"claude-sonnet-4","context_window_tokens":200000},"file_line_tracker":{},"checkpoint_manager":null,"mcp_enabled":true}


