Full Module Reference

The public default path for Company is sections -> show -> trace. This document provides a reference for the report/docs source namespace one level below.

Warning: c.report.extract(topic) is an internal API. For everyday analysis, use c.show(topic). The c.report.extract() calls in the examples below are deep access patterns using c.report.extract(topic) or c.docs.notes.

# Recommended path
c.show("dividend")           # show() applies source priority
c.show("audit")

# Deep access (report namespace directly)
c.report.extract("dividend") # report Result object
c.docs.notes.inventory       # K-IFRS notes directly

Dividends, Shareholders & Capital Structure

c.dividend — Dividends

Dividend time series. DPS, payout ratio, dividend yield, etc.

c.dividend
# year | netIncome | eps | totalDividend | payoutRatio | dividendYield | dps | dpsPreferred

Result attributes:

Usage example:

# Recent dividend info
div = c.dividend
if div is not None:
    last = div.row(-1, named=True)
    print(f"DPS: {last['dps']}원, 배당수익률: {last['dividendYield']}%")

c.majorHolder — Largest Shareholder

Time series of the largest shareholder and related parties’ ownership ratios.

c.majorHolder
# year | majorHolder | majorRatio | totalRatio | holderCount | ...

Access individual shareholder list via report namespace:

result = c.report.extract("majorHolder")  # or c.show("majorHolder")
result.majorHolder   # "이재용"
result.majorRatio    # 20.76
result.totalRatio    # Total related parties ownership ratio
result.holders       # list[Holder] — Individual shareholder list

Holder attributes:

Usage example:

result = c.report.extract("majorHolder")
print(f"최대주주: {result.majorHolder} ({result.majorRatio}%)")
for h in result.holders:
    print(f"  {h.name} ({h.relation}): {h.ratioEnd}%")

c.holderOverview — Shareholder Overview

Comprehensive overview of 5%+ shareholders, minority shareholders, and voting rights. Returns a Result object directly.

result = c.holderOverview

result.bigHolders    # list[BigHolder] — Shareholders with 5%+ ownership
result.minority      # Minority — Minority shareholder status
result.voting        # VotingRights — Voting rights status

Usage example:

result = c.holderOverview
for bh in result.bigHolders:
    print(f"{bh.name}: {bh.ratio}%")
print(f"소액주주 비율: {result.minority.ratio}%")

c.shareCapital — Total Shares

Time series of authorized, issued, treasury, and outstanding shares.

c.shareCapital
# year | authorizedShares | issuedShares | treasuryShares | outstandingShares | ...

Full access via report namespace:

result = c.report.extract("shareCapital")
result.authorizedShares    # Total authorized shares
result.issuedShares        # Total issued shares
result.treasuryShares      # Treasury shares
result.outstandingShares   # Outstanding shares
result.treasuryRatio       # Treasury share ratio
result.timeSeries          # Time series DataFrame

c.capitalChange — Capital Changes

Changes in paid-in capital, total shares, and treasury shares.

c.capitalChange
# year | commonShares | preferredShares | commonParValue | ...

Additional DataFrames via report namespace:

result = c.report.extract("capitalChange")
result.capitalDf     # Paid-in capital change time series
result.shareTotalDf  # Total shares time series
result.treasuryDf    # Treasury shares change time series

c.fundraising — Capital Increases/Decreases

Capital increase (decrease) history.

c.fundraising
# date | issueType | stockType | quantity | parValue | issuePrice | note

Executives & Governance

c.executive — Executive Status

Aggregate time series of registered executives.

c.executive
# year | totalRegistered | insideDirectors | outsideDirectors | otherNonexec | maleCount | femaleCount

Access unregistered executive compensation via report namespace:

result = c.report.extract("executive")
result.executiveDf   # Registered executive aggregate time series
result.unregPayDf    # Unregistered executive compensation time series

executiveDf key columns:

c.executivePay — Executive Compensation

Compensation time series by type (registered directors / outside directors / audit committee members).

c.executivePay
# year | category | headcount | totalPay | avgPay

Individual compensation exceeding 500 million KRW via report namespace:

result = c.report.extract("executivePay")
result.payByTypeDf   # Compensation by type time series
result.topPayDf      # Individual compensation exceeding 500M KRW

c.show(“board”) — Board of Directors

Alias: board → boardOfDirectors

Board composition, meeting frequency, and attendance rate time series.

c.show("board")
# year | totalDirectors | outsideDirectors | meetingCount | avgAttendanceRate

Committee composition via report namespace:

result = c.report.extract("boardOfDirectors")
result.boardDf      # Board time series
result.committeeDf  # Committee composition (committeeName, composition, members)

c.audit — Audit Opinion

Audit firm, audit opinion, and key audit matters time series.

c.audit
# year | auditor | opinion | keyAuditMatters

Audit fees via report namespace:

result = c.report.extract("audit")
result.opinionDf   # Audit opinion time series
result.feeDf       # Audit fee time series (auditor, contractFee, contractHours, actualFee, actualHours)

c.auditSystem — Audit System

Audit committee composition and audit activity records.

c.auditSystem
# name | role | detail

Audit activities via report namespace:

result = c.report.extract("auditSystem")
result.committeeDf   # Audit committee composition
result.activityDf    # Audit activity records (date, agenda, result)

c.show(“control”) — Internal Controls

Alias: control → internalControl

Evaluation of internal accounting control systems.

c.show("control")
# year | opinion | auditor | hasWeakness

c.shareholderMeeting — Shareholder Meeting

Shareholder meeting agendas and resolution results.

c.shareholderMeeting
# agenda | result

Risk & Regulation

c.show(“contingent”) — Contingent Liabilities

Alias: contingent → contingentLiability

Debt guarantees and litigation status.

c.show("contingent")
# year | totalGuaranteeAmount | lineCount

Litigation status via report namespace:

result = c.report.extract("contingentLiability")
result.guaranteeDf  # Debt guarantee time series
result.lawsuitDf    # Litigation status (filingDate, parties, description, amount, status)

c.show(“relatedParty”) — Related Party Transactions

Alias: relatedParty → relatedPartyTx

Transactions with major shareholders and related parties.

c.show("relatedParty")
# year | entity | sales | purchases

Debt guarantees and asset transactions via report namespace:

result = c.report.extract("relatedPartyTx")
result.revenueTxDf   # Revenue transaction time series
result.guaranteeDf   # Debt guarantee time series
result.assetTxDf     # Asset transaction time series

c.sanction — Sanctions

Sanctions and penalties history.

c.sanction
# year | date | agency | subject | action | amount | reason

c.show(“risk”) — Risk Management

Alias: risk → riskDerivative

FX sensitivity and derivative contract status.

c.show("risk")
# currency | upImpact | downImpact

Derivatives via report namespace:

result = c.report.extract("riskDerivative")
result.fxDf          # FX sensitivity
result.derivativeDf  # Derivative contracts

Business, Products & Revenue

c.employee — Employee Status

Time series of headcount, average salary, and tenure.

c.employee
# year | totalEmployees | avgTenure | totalSalary | avgSalary

Usage example:

emp = c.employee
if emp is not None:
    last = emp.row(-1, named=True)
    print(f"직원수: {last['totalEmployees']:,}명, 평균연봉: {last['avgSalary']:,.0f}백만원")

c.productService — Key Products

Key products and services status.

c.productService
# label | amount | ratio

c.salesOrder — Sales/Orders

Sales performance (by segment/product) and order backlog.

c.salesOrder
# label | v1 | v2 | v3

Order status via report namespace:

result = c.report.extract("salesOrder")
result.salesDf   # Sales performance
result.orderDf   # Order backlog

c.rnd — R&D

R&D expenses and ratio to revenue.

c.rnd
# year | rndExpense | revenueRatio

Finance & Assets

c.subsidiary — Subsidiaries

Comprehensive time series of investments in other companies.

c.subsidiary
# year | totalCount | listedCount | unlistedCount | totalBook

Individual investee companies via report namespace:

result = c.report.extract("subsidiary")
for inv in result.investments[:5]:
    print(f"{inv.name}: {inv.endRatio}%, 장부가 {inv.endBook}")

c.bond — Debt Securities

Corporate bonds, commercial paper, and other debt securities issuance records.

c.bond
# year | totalIssuances | totalAmount | unredeemedCount

Individual issuance details via report namespace:

result = c.report.extract("bond")
for b in result.issuances:
    print(f"{b.bondType} | {b.amount}백만원 | {b.interestRate} | {b.rating}")

c.otherFinance — Other Financial Items

Allowance for doubtful accounts and inventory status.

c.otherFinance
# account | period | totalDebt | provision

Inventory via report namespace:

result = c.report.extract("otherFinance")
result.badDebtDf     # Allowance for doubtful accounts
result.inventoryDf   # Inventory status

c.investmentInOther — Investments in Other Companies

Investment status in other companies.

c.investmentInOther
# name | 상장여부 | 최초취득일자 | 출자목적 | ...

Company Information

c.companyOverviewDetail — Company Basic Information

Returns a dict (not a DataFrame).

info = c.companyOverviewDetail

info["foundedDate"]    # "1969-01-13"
info["listedDate"]     # "1975-06-11"
info["ceo"]            # "한종희"
info["address"]        # Headquarters address
info["mainBusiness"]   # Main business
info["website"]        # Homepage URL

c.affiliateGroup — Affiliates

Affiliate company status.

c.affiliateGroup
# name | listed

Additional info via report namespace:

result = c.report.extract("affiliateGroup")
result.groupName      # "삼성"
result.listedCount    # Number of listed affiliates
result.unlistedCount  # Number of unlisted affiliates
result.totalCount     # Total number of affiliates
result.affiliateDf    # Affiliates DataFrame

c.companyHistory — Company History

Company history events.

c.companyHistory
# date | event

c.articlesOfIncorporation — Articles of Incorporation

Amendment history and business purpose status.

c.articlesOfIncorporation
# date | meetingName | changes | reason

Business purposes via report namespace:

result = c.report.extract("articlesOfIncorporation")
result.changesDf    # Amendment history
result.purposesDf   # Business purposes (purpose, active)

Disclosure Text

Recommended path: Access narrative disclosure text through sections-based show() such as c.show("businessOverview"), c.show("overview"), etc. The c.business and c.mdna below are legacy access paths via the docs namespace.

c.docs.business — Business Description

biz = c.docs.business
for s in biz:
    print(f"[{s.key}] {s.title} ({s.chars} chars)")
    print(s.text[:200])

Key section keys:

c.docs.mdna — MD&A

Management’s discussion and analysis.

c.docs.mdna   # Business overview text

c.docs.overview — Company Overview

Quantitative data including founding date, address, credit ratings, etc. Returns a Result object.

result = c.docs.overview

result.founded          # Founding year
result.address          # Location
result.homepage         # Website
result.subsidiaryCount  # Number of subsidiaries
result.isSME            # Whether SME
result.isVenture        # Whether venture company
result.listedDate       # Listing date

Check credit ratings:

for cr in result.creditRatings:
    print(f"{cr.agency}: {cr.grade}")

Check parsing status:

print(f"Not in original: {result.missing}")
print(f"Parse failed: {result.failed}")

c.docs.rawMaterial — Raw Materials/Equipment

Raw material procurement, tangible assets, and capital expenditure status. Returns a Result object.

result = c.docs.rawMaterial

result.materials     # list[RawMaterial] — Raw material list
result.equipment     # Equipment — Tangible asset status
result.capexItems    # list[CapexItem] — Capital expenditure items

Usage example:

result = c.rawMaterial
for m in result.materials:
    print(f"{m.item}: {m.amount}백만원 ({m.ratio}%) — {m.supplier}")

eq = result.equipment
print(f"합계: {eq.total}, CAPEX: {eq.capex}")

K-IFRS Notes

Access via c.notes. See the Notes section in API Overview for details.

12 Key Items

c.notes.inventory          # Inventory DataFrame
c.notes.receivables        # Trade receivables
c.notes.borrowings         # Borrowings
c.notes.tangibleAsset      # Tangible asset movement schedule
c.notes.intangibleAsset    # Intangible assets
c.notes.provisions         # Provisions
c.notes.eps                # Earnings per share
c.notes.lease              # Leases
c.notes.investmentProperty # Investment property
c.notes.affiliates         # Associates
c.notes.segments           # Segment information
c.notes.costByNature       # Expenses by nature

Korean Key Access

c.notes["재고자산"]         # Same as c.notes.inventory

Tangible Asset Movement Schedule

result = c.notes.tangibleAsset
result.reliability    # "high" or "low"
result.warnings       # Reliability warnings
result.movementDf     # Category-level opening/closing time series

Segment Information

result = c.notes.segments
result.revenue   # Segment revenue time series DataFrame
for year, tables in result.tables.items():
    for t in tables:
        print(f"[{year}] {t.tableType}: {t.columns}")

Expenses by Nature

result = c.notes.costByNature
result.timeSeries    # Expense item time series
result.ratios        # Composition ratios
result.crossCheck    # Cross-check results

Direct Lookup by Unregistered Keyword

c.notes.detail("법인세")       # NotesDetail result
c.notes.detail("법인세").tableDf

c.notes.detail("특수관계자")
c.notes.detail("특수관계자").tableDf

All 23 keywords: 재고자산, 주당이익, 충당부채, 차입금, 매출채권, 리스, 투자부동산, 무형자산, 법인세, 특수관계자, 약정사항, 금융자산, 공정가치, 이익잉여금, 금융부채, 기타포괄손익, 사채, 종업원급여, 퇴직급여, 확정급여, 재무위험, 우발부채, 담보