eddmpython/dartlab-data

DartLab Data

Structured company data from DART & EDGAR disclosure filings

What is this?

Pre-collected Parquet files from 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

With DartLab (recommended)

pip install dartlab

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

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)

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

Data Source

• DART (Korea): dart.fss.or.kr — Korea's electronic disclosure system operated by the Financial Supervisory Service
• EDGAR (US): 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.

Support

If DartLab is useful for your work, consider supporting the project:

• GitHub Issues — bug reports, feature requests
• Blog — 120+ articles on Korean disclosure analysis