Duplicate from eddmpython/dartlab-data

Co-authored-by: python <eddmpython@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.avro filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+pyodide/dartlab-0.9.8-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+pyodide/dartlab-0.9.9-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+pyodide/dartlab-0.9.10-py3-none-any.whl filter=lfs diff=lfs merge=lfs -text
+dart/contentIndex/main_stems.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+task_categories:
+  - table-question-answering
+  - text-classification
+language:
+  - ko
+  - en
+tags:
+  - finance
+  - disclosure
+  - dart
+  - edgar
+  - sec
+  - xbrl
+  - korea
+  - financial-statements
+  - corporate-filings
+  - 전자공시
+  - 재무제표
+  - 사업보고서
+  - 한국
+pretty_name: DartLab 전자공시 데이터
+size_categories:
+  - 1K<n<10K
+---
+
+<div align="center">
+
+<br>
+
+<img alt="DartLab" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/logo.png" width="160">
+
+<h3>DartLab Data</h3>
+
+<p><b>Structured company data from DART & EDGAR disclosure filings</b></p>
+<p>DART 전자공시 + EDGAR 공시 데이터 — 한국 2,700사 / 미국 970사</p>
+
+<p>
+<a href="https://github.com/eddmpython/dartlab"><img src="https://img.shields.io/badge/GitHub-dartlab-ea4647?style=for-the-badge&labelColor=050811&logo=github&logoColor=white" alt="GitHub"></a>
+<a href="https://pypi.org/project/dartlab/"><img src="https://img.shields.io/pypi/v/dartlab?style=for-the-badge&color=ea4647&labelColor=050811&logo=pypi&logoColor=white" alt="PyPI"></a>
+<a href="https://eddmpython.github.io/dartlab/"><img src="https://img.shields.io/badge/Docs-GitHub_Pages-38bdf8?style=for-the-badge&labelColor=050811&logo=github-pages&logoColor=white" alt="Docs"></a>
+<a href="https://buymeacoffee.com/dartlab"><img src="https://img.shields.io/badge/Sponsor-Buy_Me_A_Coffee-ffdd00?style=for-the-badge&labelColor=050811&logo=buy-me-a-coffee&logoColor=white" alt="Sponsor"></a>
+</p>
+
+</div>
+
+## What is this?
+
+<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-study.png" width="120">
+
+Pre-collected [Parquet](https://parquet.apache.org/) files from [DartLab](https://github.com/eddmpython/dartlab) — a Python library that turns DART (Korea) and EDGAR (US) disclosure filings into one structured company map.
+
+한국 DART 전자공시 시스템과 미국 SEC EDGAR에서 수집한 기업 공시 데이터입니다.
+
+This dataset is the **data layer** behind DartLab. When you run `dartlab.Company("005930")`, the library automatically downloads the relevant parquet from this repo.
+
+## Dataset Structure
+
+```
+dart/
+├── docs/          2,547 companies  ~8 GB      disclosure text (sections, tables, markdown)
+├── finance/       2,744 companies  ~586 MB    financial statements (BS, IS, CF, XBRL)
+└── report/        2,711 companies  ~319 MB    structured disclosure APIs (28 types)
+```
+
+Each file is one company: `{stockCode}.parquet`
+
+### docs — Disclosure Text
+
+Full-text sections from annual/quarterly reports, parsed into structured blocks.
+
+| Column | Description |
+|--------|------------|
+| `rcept_no` | DART filing ID |
+| `rcept_date` | Filing date |
+| `stock_code` | Stock code |
+| `corp_name` | Company name |
+| `report_type` | Annual/quarterly report type |
+| `section_title` | Original section title |
+| `section_order` | Section ordering |
+| `content` | Section text (markdown) |
+| `blockType` | `text` / `table` / `heading` |
+| `year` | Filing year |
+
+### finance — Financial Statements
+
+XBRL-based financial data from DART OpenAPI (`fnlttSinglAcntAll`).
+
+| Column | Description |
+|--------|------------|
+| `bsns_year` | Business year |
+| `reprt_code` | Report quarter code |
+| `stock_code` | Stock code |
+| `corp_name` | Company name |
+| `fs_div` | `CFS` (consolidated) / `OFS` (separate) |
+| `sj_div` | Statement type (BS/IS/CF/SCE) |
+| `account_id` | XBRL account ID |
+| `account_nm` | Account name (Korean) |
+| `thstrm_amount` | Current period amount |
+| `frmtrm_amount` | Prior period amount |
+| `bfefrmtrm_amount` | Two periods prior amount |
+
+### report — Structured Disclosure APIs
+
+28 DART API categories covering governance, compensation, shareholding, and more.
+
+| Column | Description |
+|--------|------------|
+| `apiType` | API category (e.g., `dividend`, `employee`, `executive`) |
+| `year` | Year |
+| `quarter` | Quarter |
+| `stockCode` | Stock code |
+| `corpCode` | DART corp code |
+| *(varies)* | Category-specific columns |
+
+**28 API types:** dividend, employee, executive, majorHolder, treasuryStock, capitalChange, auditOpinion, stockTotal, outsideDirector, corporateBond, and more.
+
+## Learn More
+
+<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-analyze.png" width="120">
+
+DartLab auto-downloads from this dataset — one stock code gives you the full company map. Start with the intro below.
+
+<div align="center">
+
+<a href="https://www.youtube.com/shorts/97lYLWMWzvA"><img src="https://img.youtube.com/vi/97lYLWMWzvA/maxresdefault.jpg" alt="DartLab 30s Demo" width="320"></a>
+
+<sub>▶ <a href="https://www.youtube.com/shorts/97lYLWMWzvA">DartLab 30s Demo</a></sub>
+
+</div>
+
+- **GitHub** — [github.com/eddmpython/dartlab](https://github.com/eddmpython/dartlab)
+- **Intro blog** — [DartLab 시작하기 / Getting started](https://eddmpython.github.io/dartlab/blog/dartlab-easy-start)
+- **Docs** — [eddmpython.github.io/dartlab](https://eddmpython.github.io/dartlab/)
+- **YouTube** — [@eddmpython](https://www.youtube.com/@eddmpython)
+
+<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-discover.png" width="120">
+
+## Data Source
+
+- **DART** (Korea): [dart.fss.or.kr](https://dart.fss.or.kr) — Korea's electronic disclosure system operated by the Financial Supervisory Service
+- **EDGAR** (US): [sec.gov/edgar](https://www.sec.gov/edgar) — SEC's Electronic Data Gathering, Analysis, and Retrieval system
+
+All data is sourced from public government disclosure systems. Financial figures are preserved as-is from the original filings — no rounding, no estimation, no interpolation.
+
+## Update Schedule
+
+This dataset is updated automatically via GitHub Actions (daily). Recent filings (last 7 days) are checked and collected incrementally.
+
+## License
+
+Apache 2.0 — same as [DartLab](https://github.com/eddmpython/dartlab).
+
+## Support
+
+If DartLab is useful for your work, consider supporting the project:
+
+[![Buy Me A Coffee](https://img.shields.io/badge/Buy_Me_A_Coffee-Support-ffdd00?style=for-the-badge&labelColor=050811&logo=buy-me-a-coffee&logoColor=white)](https://buymeacoffee.com/dartlab)
+
+- [GitHub Issues](https://github.com/eddmpython/dartlab/issues) — bug reports, feature requests
+- [Blog](https://eddmpython.github.io/dartlab/blog/) — 120+ articles on Korean disclosure analysis

ai/knowledge/error_patterns.json

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+[
+  {
+    "error_type": "TypeError",
+    "error_signature": "analysis() takes <N> positional arguments but 1 was given",
+    "wrong_code": "c.analysis(\"수익성\")",
+    "correct_code": "c.analysis(\"financial\", \"수익성\")",
+    "tool_name": "analysis",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "TypeError",
+    "error_signature": "analysis() missing 1 required positional argument",
+    "wrong_code": "c.analysis(\"성장성\")",
+    "correct_code": "c.analysis(\"financial\", \"성장성\")",
+    "tool_name": "analysis",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "TypeError",
+    "error_signature": "macro() got an unexpected keyword argument 'topic'",
+    "wrong_code": "dartlab.macro(topic=\"종합\")",
+    "correct_code": "dartlab.macro(\"종합\")",
+    "tool_name": "macro",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "TypeError",
+    "error_signature": "macro() got an unexpected keyword argument 'axis'",
+    "wrong_code": "dartlab.macro(axis=\"사이클\")",
+    "correct_code": "dartlab.macro(\"사이클\")",
+    "tool_name": "macro",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "KeyError",
+    "error_signature": "KeyError: 'cycle'",
+    "wrong_code": "result[\"cycle\"]",
+    "correct_code": "print(result.keys())  # 먼저 키 확인",
+    "tool_name": "macro",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "KeyError",
+    "error_signature": "KeyError: 'factors'",
+    "wrong_code": "result[\"factors\"]",
+    "correct_code": "print(result.keys())  # 먼저 키 확인",
+    "tool_name": "macro",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "KeyError",
+    "error_signature": "KeyError: 'narrative'",
+    "wrong_code": "result[\"narrative\"]",
+    "correct_code": "print(result.keys())  # 먼저 키 확인",
+    "tool_name": "macro",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "AttributeError",
+    "error_signature": "has no attribute 'empty'",
+    "wrong_code": "df.empty",
+    "correct_code": "len(df) == 0  # Polars는 .empty 없음",
+    "tool_name": "polars",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "AttributeError",
+    "error_signature": "has no attribute 'iterrows'",
+    "wrong_code": "df.iterrows()",
+    "correct_code": "df.iter_rows(named=True)  # Polars 문법",
+    "tool_name": "polars",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "AttributeError",
+    "error_signature": "has no attribute 'sort_values'",
+    "wrong_code": "df.sort_values(\"col\")",
+    "correct_code": "df.sort(\"col\")  # Polars 문법",
+    "tool_name": "polars",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "AttributeError",
+    "error_signature": "has no attribute 'to_dict'",
+    "wrong_code": "df.to_dict()",
+    "correct_code": "df.to_dicts()  # Polars는 to_dicts()",
+    "tool_name": "polars",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "MemoryError",
+    "error_signature": "c.sections",
+    "wrong_code": "c.sections",
+    "correct_code": "c.show(\"IS\")  # sections는 409MB. show(topic)으로 개별 조회",
+    "tool_name": "company",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "TimeoutError",
+    "error_signature": "scan join timeout",
+    "wrong_code": "df1.join(df2, ...)",
+    "correct_code": "# scan DataFrame join 금지 (타임아웃). 개별 scan 결과를 순차 해석",
+    "tool_name": "scan",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "TypeError",
+    "error_signature": "review() should be used only for explicit report requests",
+    "wrong_code": "c.review(\"수익성\")",
+    "correct_code": "c.analysis(\"financial\", \"수익성\")  # 분석에는 analysis 사용",
+    "tool_name": "review",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  },
+  {
+    "error_type": "AttributeError",
+    "error_signature": "has no attribute 'rename'",
+    "wrong_code": "df.rename({\"old\": \"new\"})",
+    "correct_code": "df.rename({\"old\": \"new\"})  # Polars rename은 동일하나 columns= 불필요",
+    "tool_name": "polars",
+    "frequency": 5,
+    "last_seen": 1775313437.0288768
+  }
+]

ai/knowledge/meta.json

@@ -0,0 +1,8 @@
+{
+  "version": 1,
+  "exported_at": 1776066637.9972532,
+  "stats": {
+    "insights": 150,
+    "playbook": 1544
+  }
+}

ai/knowledge/skills.json

@@ -0,0 +1,98 @@
+[
+  {
+    "question": "삼성전자 수익성 분석해줘",
+    "category": "profitability",
+    "tools_used": "[\"analysis\"]",
+    "code_template": "c = dartlab.Company(\"{stockCode}\")\nr = c.analysis(\"financial\", \"수익성\")\nflags = r.get(\"profitabilityFlags\", {})\nhistory = r[\"marginTrend\"][\"history\"][:5]\nprint(\"| 기간 | 매출(억) | 영업이익률 | 순이익률 |\")\nprint(\"| --- | --- | --- | --- |\")\nfor h in history:\n    rev = h.get(\"revenue\", 0)\n    opm = h.get(\"operatingMargin\", 0)\n    npm = h.get(\"netMargin\", 0)\n    print(f'| {h[\"period\"]} | {rev/1e8:,.0f} | {opm:.1f}% | {npm:.1f}% |')",
+    "result_keys": "[\"marginTrend\", \"profitabilityFlags\", \"returnTrend\"]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.7738278,
+    "last_used": 1775314511.7738278
+  },
+  {
+    "question": "신용등급 분석해줘",
+    "category": "credit",
+    "tools_used": "[\"credit\"]",
+    "code_template": "c = dartlab.Company(\"{stockCode}\")\ncr = c.credit(detail=True)\nprint(f\"등급: {cr['grade']}, 건전도: {cr['healthScore']}/100\")\nprint(f\"\\n주요 지표:\")\nfor k, v in cr.get(\"metrics\", {}).items():\n    print(f\"  {k}: {v}\")",
+    "result_keys": "[\"grade\", \"healthScore\", \"score\", \"metrics\", \"narratives\"]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.7953985,
+    "last_used": 1775314511.7953985
+  },
+  {
+    "question": "시장에서 수익성 좋은 회사 찾아줘",
+    "category": "profitability",
+    "tools_used": "[\"scan\"]",
+    "code_template": "df = dartlab.scan(\"profitability\")\nprint(df.columns)\nprint(df.head(10))",
+    "result_keys": "[]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.8073995,
+    "last_used": 1775314511.8073995
+  },
+  {
+    "question": "경제 사이클 분석해줘",
+    "category": "macro",
+    "tools_used": "[\"macro\"]",
+    "code_template": "r = dartlab.macro(\"사이클\")\nprint(r.keys())\nfor k, v in r.items():\n    if isinstance(v, dict):\n        print(f\"\\n{k}:\")\n        for k2, v2 in v.items():\n            print(f\"  {k2}: {v2}\")\n    else:\n        print(f\"{k}: {v}\")",
+    "result_keys": "[]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.8153992,
+    "last_used": 1775314511.8153992
+  },
+  {
+    "question": "최근 주가 추이 보여줘",
+    "category": "quant",
+    "tools_used": "[\"gather\"]",
+    "code_template": "c = dartlab.Company(\"{stockCode}\")\nprice = c.gather(\"price\")\nif price is not None:\n    print(price.tail(20))\nelse:\n    print(\"주가 데이터 없음\")",
+    "result_keys": "[]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.82488,
+    "last_used": 1775314511.82488
+  },
+  {
+    "question": "공시 검색",
+    "category": "search",
+    "tools_used": "[\"search\"]",
+    "code_template": "results = dartlab.search(\"유상증자\")\nprint(f\"검색 결과: {len(results)}건\")\nprint(results.head(10))",
+    "result_keys": "[]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.8328826,
+    "last_used": 1775314511.8328826
+  },
+  {
+    "question": "종합 분석해줘",
+    "category": "general",
+    "tools_used": "[\"analysis\"]",
+    "code_template": "c = dartlab.Company(\"{stockCode}\")\n# 3축 동시 수집\nprof = c.analysis(\"financial\", \"수익성\")\ngrowth = c.analysis(\"financial\", \"성장성\")\nstab = c.analysis(\"financial\", \"안정성\")\n\n# 수익성 요약\nh = prof[\"marginTrend\"][\"history\"][0]\nprint(f\"최근 영업이익률: {h.get('operatingMargin', 0):.1f}%\")\n\n# 성장성 요약\ng = growth.get(\"revenueGrowth\", {})\nprint(f\"매출 성장률: {g.get('yoy', 'N/A')}\")\n\n# 안정성 요약\ns = stab.get(\"debtAnalysis\", {})\nprint(f\"부채비율: {s.get('debtRatio', 'N/A')}\")",
+    "result_keys": "[\"marginTrend\", \"revenueGrowth\", \"debtAnalysis\"]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.842193,
+    "last_used": 1775314511.842193
+  },
+  {
+    "question": "재고자산 주석 분석",
+    "category": "general",
+    "tools_used": "[]",
+    "code_template": "c = dartlab.Company(\"{stockCode}\")\ninv = c.notes.inventory\nif inv is not None:\n    print(inv)\nelse:\n    print(\"재고자산 주석 데이터 없음\")",
+    "result_keys": "[]",
+    "success_count": 1,
+    "quality_score": 0.8,
+    "mode": "analysis",
+    "created_at": 1775314511.8515372,
+    "last_used": 1775314511.8515372
+  }
+]

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