README.md

---
license: mit
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>
<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/eddmpython"><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.

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.

## Usage

<img align="right" src="https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/assets/avatar-analyze.png" width="120">

### With DartLab (recommended)

```bash
pip install dartlab
```

```python
import dartlab

c = dartlab.Company("005930")   # Samsung Electronics
c.sections                      # full company map (topic x period)
c.BS                            # balance sheet
c.ratios                        # financial ratios
c.show("businessOverview")      # narrative text

# US companies work the same way
us = dartlab.Company("AAPL")
us.BS
us.ratios
```

DartLab auto-downloads from this dataset. No manual download needed.

### Direct download

```python
import polars as pl

# Single file
url = "https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/dart/finance/005930.parquet"
df = pl.read_parquet(url)
```

```bash
# wget
wget https://huggingface.co/datasets/eddmpython/dartlab-data/resolve/main/dart/finance/005930.parquet
```

<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

MIT — 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/eddmpython)

- [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