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Financial Statements: Income, Balance Sheet, and Cash Flow from SEC Filings

Get financial statements from SEC filings in Python

Quick Start

Three lines to Apple's income statement:

from edgar import Company

company = Company("AAPL")
financials = company.get_financials()
income_statement = financials.income_statement()

AAPL Income Statement

That's it. You now have Apple's full income statement from their latest 10-K filing.

See Apple's financials on edgar.tools — no code required →

Get Specific Values

Need just one number? Use the convenience methods:

financials = company.get_financials()

revenue = financials.get_revenue()
net_income = financials.get_net_income()
total_assets = financials.get_total_assets()
Method Returns
get_revenue() Total revenue / net sales
get_net_income() Net income
get_operating_income() Operating income / loss
get_total_assets() Total assets
get_total_liabilities() Total liabilities
get_stockholders_equity() Stockholders' equity
get_operating_cash_flow() Operating cash flow
get_free_cash_flow() Free cash flow
get_capital_expenditures() Capital expenditures
get_current_assets() Current assets
get_current_liabilities() Current liabilities

All methods accept period_offset to access prior periods (0=current, 1=previous).

Available Statements

financials = company.get_financials()

income = financials.income_statement()
balance = financials.balance_sheet()
cashflow = financials.cashflow_statement()
equity = financials.statement_of_equity()
comprehensive = financials.comprehensive_income()
Statement Method
Income Statement income_statement()
Balance Sheet balance_sheet()
Cash Flow Statement cashflow_statement()
Statement of Equity statement_of_equity()
Comprehensive Income comprehensive_income()

Cash flow naming

The method is cashflow_statement() (no underscore between "cash" and "flow"). The Company object also accepts company.cash_flow() as an alias, but on the Financials object always use cashflow_statement().

Export to DataFrame

Every statement converts to a pandas DataFrame:

income = financials.income_statement()

# Convert to DataFrame
df = income.to_dataframe()

# Export to CSV
df.to_csv("apple_income_statement.csv")

# Export to Excel
df.to_excel("apple_income_statement.xlsx")

The DataFrame preserves the statement structure with labeled rows and period columns.

Quarterly Financials

Use get_quarterly_financials() for 10-Q data:

quarterly = company.get_quarterly_financials()
income = quarterly.income_statement()

AAPL Quarterly Income Statement

Need Method
Annual (10-K) company.get_financials()
Quarterly (10-Q) company.get_quarterly_financials()

Getting Different Levels of Detail

Financial statements can show different levels of detail. Use the view parameter to control what you see:

financials = company.get_financials()

# Summary: Matches SEC Viewer (~15-20 rows)
income = financials.income_statement(view="summary")

# Standard: Matches the filing document (default)
income = financials.income_statement(view="standard")

# Detailed: All dimensional breakdowns
income = financials.income_statement(view="detailed")

Summary View

AAPL Detailed Income Statement

Detailed View

AAPL Detailed Income Statement

View Shows Typical Rows Best For
"summary" Matches SEC Viewer ~15-20 Quick overview, validation
"standard" Matches filing document ~25-35 Display, full context
"detailed" All dimensional breakdowns ~50+ Data extraction, segment analysis

Example: Apple Revenue

With view="summary": 

Revenue                              $391,035M

With view="standard": 

Revenue:
  Products                           $298,085M
  Services                            $92,950M

With view="detailed": 

Revenue:
  Products                           $298,085M
    iPhone                           $201,183M
    Mac                               $29,357M
    iPad                              $26,694M
    Wearables, Home and Accessories   $40,851M
  Services                            $92,950M

Views with DataFrames

The view parameter also works when exporting to DataFrame:

income = financials.income_statement()

df_summary = income.to_dataframe(view="summary")
df_standard = income.to_dataframe(view="standard")
df_detailed = income.to_dataframe(view="detailed")

When to Use Each View

  • Summary: Quick checks, matches SEC Viewer, comparing many companies
  • Standard: Matches the filing document, full context with face-level dimensions
  • Detailed: Data extraction, segment analysis, complete dimensional breakdowns

Learn more: Dimension Handling Guide

Compare Multiple Periods

To analyze trends across multiple filings, use XBRLS:

from edgar.xbrl import XBRLS

# Get last 3 annual filings
filings = company.get_filings(form="10-K").head(3)

# Stitch them together
xbrls = XBRLS.from_filings(filings)

# Get income statement across all periods
income = xbrls.statements.income_statement()

# Use view="detailed" for dimensional breakdowns across periods
income_detailed = xbrls.statements.income_statement(view="detailed")

The view parameter works the same as on single-period statements ("standard", "detailed", "summary"). This aligns the periods and concepts across filings for easy comparison.

Learn more: Multi-Period Analysis Guide

Why skip amendments?

Use amendments=False when fetching filings. Amended filings (10-K/A) sometimes contain only the corrected sections, not complete financial statements.

Advanced Topics

Raw Facts Query

Query individual XBRL facts for research or custom calculations:

xbrl = filing.xbrl()

# Find all revenue facts
revenue_facts = xbrl.facts.query()\
    .by_concept("Revenue")\
    .to_dataframe()

# Search by label
rd_facts = xbrl.facts.query()\
    .by_label("Research", exact=False)\
    .to_dataframe()

Learn more: XBRL Querying Guide

Troubleshooting

"No financial data found"

Some companies (especially newer or smaller ones) may not have XBRL data:

filing = company.get_filings(form="10-K").latest()
if filing.xbrl():
    print("XBRL available")
else:
    print("No XBRL - try filing.text() for raw content")

"Statement is empty"

Try using the detailed view to include all dimensional data:

df = income.to_dataframe(view="detailed")

"Numbers don't match the SEC website"

Check that you're looking at the right period:

xbrl = filing.xbrl()
print(xbrl.reporting_periods)

API Quick Reference

Company-Level (Easiest)

Method Description
company.get_financials() Latest annual financials (10-K)
company.get_quarterly_financials() Latest quarterly financials (10-Q)

Financials Object

Method Description
financials.income_statement() Income statement
financials.balance_sheet() Balance sheet
financials.cashflow_statement() Cash flow statement
financials.get_revenue() Revenue value
financials.get_net_income() Net income value
financials.get_total_assets() Total assets value
financials.get_financial_metrics() Dict of all key metrics

Statement Object

Method Description
statement.to_dataframe() Convert to pandas DataFrame
statement.to_dataframe(view="summary") Matches SEC Viewer
statement.to_dataframe(view="standard") Matches filing document
statement.to_dataframe(view="detailed") All dimensional breakdowns

Filing-Level (More Control)

Method Description
filing.xbrl() Parse XBRL from filing
xbrl.statements.income_statement() Get income statement
xbrl.facts.query() Query individual facts

Multi-Period Analysis

Method Description
XBRLS.from_filings(filings) Stitch multiple filings together
xbrls.statements.income_statement() Aligned multi-period statement

Next Steps

See it on edgar.tools

The code above extracts financials programmatically. edgar.tools shows the same data visually — plus Disclosure Search, which lets you browse XBRL topics across every 10-K year for a company.

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