dart-fss

The dart-fss library provides a wrapper around the Open DART API, organized into several specialized modules based on the type of information you need to retrieve:

• Filing Information (dart_fss.api.filings): Access public disclosure information.
• Business Report Key Information (dart_fss.api.info): Retrieve key details from business reports.
• Listed Company Financial Information (dart_fss.api.finance): Access financial statements and data for listed companies.
• Shareholder Disclosure Information (dart_fss.api.shareholder): Retrieve comprehensive information regarding shareholder disclosures.

You can extract and analyze XBRL data from financial reports using the DartXbrl class. Once a report is selected, its .xbrl attribute provides access to XBRL-specific methods for retrieving financial statements and audit information.

import dart_fss as dart

# Assuming 'report' is a selected filing object
xbrl = report.xbrl

# Check if consolidated financial statements exist
exists = xbrl.exist_consolidated()

# Get audit information as a Pandas DataFrame
audit_df = xbrl.get_audit_information(lang='en')

# Extract cash flow statements
cf_list = xbrl.get_cash_flows()
# Access the first cash flow statement in the list
cf = cf_list[0]

# Convert to Pandas DataFrame
df = cf.to_DataFrame()
# Convert to Pandas DataFrame excluding classification info
df_wo_class = cf.to_DataFrame(show_class=False)

You can manage request-related configurations through the dart_fss.utils.request module, which uses a Singleton class. This allows you to control the delay between requests, set up proxies, and modify the User-Agent used for API calls.

import dart_fss as dart

# Set request delay to 0.7 seconds
dart.utils.request.set_delay(0.7)

# Configure proxies
proxies = {'http': 'http://xxxxxxxxx.xxx', 'https': 'https://xxxxxxxxx.xxx'}
dart.utils.request.set_proxies(proxies)

# Force update the User-Agent
dart.utils.request.update_user_agent(force=True)

Install the dart-fss library using pip to enable crawling and financial statement extraction from the South Korean DART (Data Analysis, Retrieval and Transfer System) website.

pip install dart-fss

To use the library, you must obtain an API key from Open DART. You can configure the key in two ways:

1. Set the DART_API_KEY environment variable.
2. Use the dart.set_api_key() method within your Python script before performing other operations.

import dart_fss as dart

api_key = 'YOUR_API_KEY_HERE'
dart.set_api_key(api_key=api_key)

This workflow demonstrates how to load the company list, find a specific company by name, extract annual consolidated financial statements starting from a specific date, and save the results to an Excel file.

import dart_fss as dart

# 1. Set API Key
api_key = 'xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
dart.set_api_key(api_key=api_key)

# 2. Load company list from DART
corp_list = dart.get_corp_list()

# 3. Search for a company (e.g., Samsung Electronics)
samsung = corp_list.find_by_corp_name('삼성전자', exactly=True)[0]

# 4. Extract annual consolidated financial statements from 2012 onwards
fs = samsung.extract_fs(bgn_de='20120101')

# 5. Save results to Excel (default location: ./fsdata)
fs.save()

This example demonstrates the full workflow: loading the corporation list, finding a specific company by its corp_code, searching for annual reports (pblntf_detail_ty='a001'), selecting a report, and extracting its XBRL data into a Pandas DataFrame.

import dart_fss as dart

# 1. Load all listed companies
corp_list = dart.get_corp_list()

# 2. Find a specific company (e.g., Samsung Electronics) by corp_code
corp_code = '00126380'
samsung = corp_list.find_by_corp_code(corp_code=corp_code)

# 3. Search for filings (e.g., annual reports since 2019)
reports = samsung.search_filings(bgn_de='20190101', pblntf_detail_ty='a001')

# 4. Select the first report and access its XBRL data
report = reports[0]
xbrl = report.xbrl

# 5. Check for consolidated statements and extract cash flows
if xbrl.exist_consolidated():
    cf = xbrl.get_cash_flows()[0]
    df = cf.to_DataFrame()
    print(df)