The following tutorial illustrates how to access and extract XBRL data from SEC filings using Python and our XBRL-to-JSON Converter API.

   

The following example provides a quick overview on how to extract XBRL data from SEC filings and convert XBRL data to JSON.

!pip -q install sec-api

from sec_api import XbrlApi

xbrlApi = XbrlApi("YOUR_API_KEY")

# 10-K HTM File URL example
htm_url="https://www.sec.gov/Archives/edgar/data/320193/000032019320000096/aapl-20200926.htm"
xbrl_json = xbrlApi.xbrl_to_json(htm_url=htm_url)

# access income statement, balance sheet and cash flow statement
print(xbrl_json["StatementsOfIncome"])
print(xbrl_json["BalanceSheets"])
print(xbrl_json["StatementsOfCashFlows"])

# 10-K XBRL File URL example
xbrl_url="https://www.sec.gov/Archives/edgar/data/1318605/000156459021004599/tsla-10k_20201231_htm.xml"
xbrl_json = xbrlApi.xbrl_to_json(xbrl_url=xbrl_url)

# 10-K accession number example
accession_no="0001564590-21-004599"
xbrl_json = xbrlApi.xbrl_to_json(accession_no=accession_no)

The XbrlApi interface provides a .xbrl_to_json() method to convert XBRL data of an SEC filing into a standardized JSON object. The XBRL-JSON output can be converted into a pandas DataFrame, exported to a CSV or Excel file, and more.

The API fetches all XML-XBRL data, merges the relevant files, such as calculation, schema and instance document, and returns a single, standardized JSON output.

Method parameters:

• htm_url (string) - The URL of the filing from which XBRL data should be extracted and converted. Example: https://www.sec.gov/Archives/edgar/data/320193/000032019320000096/aapl-20200926.htm
• xbrl_url (string) - The URL of any XML file containing XBRL information. This can be the XBRL instance document, XBRL schema file (.xsd), XBRL calculation file, XBRL definition file, XBRL label file or the XBRL presentation file of a filing.
• accession_no (string) - The accession number of the filing from which XBRL data should be extracted and converted.

Only one parameter needs to be set. For example, if you generated a local list of accession numbers, simply call .xbrl_to_json(accession_no=...) for each accession number to access the complete XBRL data in Python.

The XBRL-to-JSON Converter supports all SEC form types. Refer to the section "Supported XBRL Filing Types" on the overview page to find a complete list of supported SEC form types.

A full tutorial on how to generate multi-year financial statements from 10-K and 10-Q XBRL data is available here.

Accessing the income statements, balance sheets and cash flow statements of annual and quarterly reports disclosed in 10-K, 40-F, 10-Q and 20-F SEC filings, respectively, is as simple as calling three lines of Python code.

The below table shows the mapping between the financial statements and their corresponding keys in the XBRL-JSON output.

Financial Statement	XBRL-JSON Key
Income statement	xbrl_json["StatementsOfIncome"]
Balance sheet	xbrl_json["BalanceSheets"]
Cash flow statement	xbrl_json["StatementsOfCashFlows"]

Let's start with looking at all available keys of the converted XBRL object while using one of Apple's 10-K filings as an example.

# 10-K HTM File URL example
htm_url = "https://www.sec.gov/Archives/edgar/data/320193/000032019320000096/aapl-20200926.htm"
xbrl_json = xbrlApi.xbrl_to_json(htm_url=htm_url)

print("Keys of the XBRL-JSON dictionary of Apple's 10-K filing")
print('---------------------------------')
print(*list(xbrl_json.keys()), sep='\n')

Keys of the XBRL-JSON dictionary of Apple's 10-K filing
---------------------------------
CoverPage
StatementsOfIncome
StatementsOfComprehensiveIncome
BalanceSheets
BalanceSheetsParenthetical
StatementsOfShareholdersEquity
StatementsOfCashFlows
SummaryofSignificantAccountingPolicies
SummaryofSignificantAccountingPoliciesPolicies
SummaryofSignificantAccountingPoliciesTables
SummaryofSignificantAccountingPoliciesAdditionalInformationDetails
SummaryofSignificantAccountingPoliciesComputationofBasicandDilutedEarningsPerShareDetails
RevenueRecognition
RevenueRecognitionTables
RevenueRecognitionAdditionalInformationDetails
RevenueRecognitionDeferredRevenueExpectedTimingofRealizationDetails
RevenueRecognitionNetSalesDisaggregatedbySignificantProductsandServicesDetails
FinancialInstruments
FinancialInstrumentsTables
FinancialInstrumentsCashCashEquivalentsandMarketableSecuritiesDetails
FinancialInstrumentsAdditionalInformationDetails
FinancialInstrumentsRestrictedCashDetails
FinancialInstrumentsDerivativeInstrumentsatGrossFairValueDetails
FinancialInstrumentsPreTaxGainsandLossesofDerivativeandNonDerivativeInstrumentsDesignatedasCashFlowandNetInvestmentHedgesDetails
FinancialInstrumentsDerivativeInstrumentsDesignatedasFairValueHedgesandRelatedHedgedItemsDetails
FinancialInstrumentsNotionalAmountsandCreditRiskAmountsAssociatedwithDerivativeInstrumentsDetails
ConsolidatedFinancialStatementDetails
ConsolidatedFinancialStatementDetailsTables
ConsolidatedFinancialStatementDetailsPropertyPlantandEquipmentNetDetails
ConsolidatedFinancialStatementDetailsOtherNonCurrentLiabilitiesDetails
ConsolidatedFinancialStatementDetailsOtherIncomeExpenseNetDetails
IncomeTaxes
IncomeTaxesTables
IncomeTaxesProvisionforIncomeTaxesDetails
IncomeTaxesAdditionalInformationDetails
IncomeTaxesReconciliationoftheProvisionforIncomeTaxesDetails
IncomeTaxesSignificantComponentsofDeferredTaxAssetsandLiabilitiesDetails
IncomeTaxesAggregateChangesinGrossUnrecognizedTaxBenefitsDetails
Debt
DebtTables
DebtAdditionalInformationDetails
DebtSummaryofCashFlowsAssociatedwithCommercialPaperDetails
DebtSummaryofTermDebtDetails
DebtFuturePrincipalPaymentsforTermDebtDetails
ShareholdersEquity
ShareholdersEquityTables
ShareholdersEquityAdditionalInformationDetails
ShareholdersEquitySharesofCommonStockDetails
ComprehensiveIncome
ComprehensiveIncomeTables
ComprehensiveIncomePretaxAmountsReclassifiedfromAOCIintotheConsolidatedStatementsofOperationsDetails
ComprehensiveIncomeChangeinAOCIbyComponentDetails
BenefitPlans
BenefitPlansTables
BenefitPlansAdditionalInformationDetails
BenefitPlansRestrictedStockUnitsActivityandRelatedInformationDetails
BenefitPlansSummaryofShareBasedCompensationExpenseandtheRelatedIncomeTaxBenefitDetails
CommitmentsandContingencies
CommitmentsandContingenciesTables
CommitmentsandContingenciesChangesinAccruedWarrantiesandRelatedCostsDetails
CommitmentsandContingenciesAdditionalInformationDetails
CommitmentsandContingenciesFuturePaymentsUnderUnconditionalPurchaseObligationsDetails
SegmentInformationandGeographicData
SegmentInformationandGeographicDataTables
SegmentInformationandGeographicDataSummaryInformationbyReportableSegmentDetails
SegmentInformationandGeographicDataReconciliationofSegmentOperatingIncometotheConsolidatedStatementsofOperationsDetails
SegmentInformationandGeographicDataNetSalesDetails
SegmentInformationandGeographicDataLongLivedAssetsDetails
Leases
LeasesTables
LeasesAdditionalInformationDetails
LeasesROUAssetsandLeaseLiabilitiesDetails
LeasesLeaseLiabilityMaturitiesDetails
SelectedQuarterlyFinancialInformationUnaudited
SelectedQuarterlyFinancialInformationUnauditedTables
SelectedQuarterlyFinancialInformationUnauditedSummaryofQuarterlyFinancialInformationDetails

---

We continue with listing all US GAAP items of the income statement in xbrl_json["StatementsOfIncome"].

print("Keys of income statement in XBRL")
print('---------------------------------')
print(*list(xbrl_json["StatementsOfIncome"].keys()), sep='\n')

Keys of income statement in XBRL
---------------------------------
RevenueFromContractWithCustomerExcludingAssessedTax
CostOfGoodsAndServicesSold
GrossProfit
ResearchAndDevelopmentExpense
SellingGeneralAndAdministrativeExpense
OperatingExpenses
OperatingIncomeLoss
NonoperatingIncomeExpense
IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest
IncomeTaxExpenseBenefit
NetIncomeLoss
EarningsPerShareBasic
EarningsPerShareDiluted
WeightedAverageNumberOfSharesOutstandingBasic
WeightedAverageNumberOfDilutedSharesOutstanding

---

Now let's inspect the content of the revenue item of the income statement.

print("Revenue items of income statement in XBRL")
print('---------------------------------')
print(*list(xbrl_json["StatementsOfIncome"]["RevenueFromContractWithCustomerExcludingAssessedTax"]), sep='\n')

Keys of income statement in XBRL
---------------------------------
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'us-gaap:ProductMember'}, 'value': '220747000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'us-gaap:ProductMember'}, 'value': '213883000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'us-gaap:ProductMember'}, 'value': '225847000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'us-gaap:ServiceMember'}, 'value': '53768000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'us-gaap:ServiceMember'}, 'value': '46291000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'us-gaap:ServiceMember'}, 'value': '39748000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'value': '274515000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'value': '260174000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'value': '265595000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:IPhoneMember'}, 'value': '137781000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:IPhoneMember'}, 'value': '142381000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:IPhoneMember'}, 'value': '164888000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:MacMember'}, 'value': '28622000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:MacMember'}, 'value': '25740000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:MacMember'}, 'value': '25198000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:IPadMember'}, 'value': '23724000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:IPadMember'}, 'value': '21280000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:IPadMember'}, 'value': '18380000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:WearablesHomeandAccessoriesMember'}, 'value': '30620000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:WearablesHomeandAccessoriesMember'}, 'value': '24482000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:ProductOrServiceAxis', 'value': 'aapl:WearablesHomeandAccessoriesMember'}, 'value': '17381000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:AmericasSegmentMember'}, 'value': '124556000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:AmericasSegmentMember'}, 'value': '116914000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:AmericasSegmentMember'}, 'value': '112093000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:EuropeSegmentMember'}, 'value': '68640000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:EuropeSegmentMember'}, 'value': '60288000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:EuropeSegmentMember'}, 'value': '62420000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:GreaterChinaSegmentMember'}, 'value': '40308000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:GreaterChinaSegmentMember'}, 'value': '43678000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:GreaterChinaSegmentMember'}, 'value': '51942000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:JapanSegmentMember'}, 'value': '21418000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:JapanSegmentMember'}, 'value': '21506000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:JapanSegmentMember'}, 'value': '21733000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:RestOfAsiaPacificSegmentMember'}, 'value': '19593000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:RestOfAsiaPacificSegmentMember'}, 'value': '17788000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'us-gaap:StatementBusinessSegmentsAxis', 'value': 'aapl:RestOfAsiaPacificSegmentMember'}, 'value': '17407000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'country:US'}, 'value': '109197000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'country:US'}, 'value': '102266000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'country:US'}, 'value': '98061000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'country:CN'}, 'value': '40308000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'country:CN'}, 'value': '43678000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'country:CN'}, 'value': '51942000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2020-09-26'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'aapl:OtherCountriesMember'}, 'value': '125010000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2019-09-28'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'aapl:OtherCountriesMember'}, 'value': '114230000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2017-10-01', 'endDate': '2018-09-29'}, 'segment': {'dimension': 'srt:StatementGeographicalAxis', 'value': 'aapl:OtherCountriesMember'}, 'value': '115592000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2020-06-28', 'endDate': '2020-09-26'}, 'value': '64698000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2020-03-29', 'endDate': '2020-06-27'}, 'value': '59685000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-12-29', 'endDate': '2020-03-28'}, 'value': '58313000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-09-29', 'endDate': '2019-12-28'}, 'value': '91819000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-06-30', 'endDate': '2019-09-28'}, 'value': '64040000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2019-03-31', 'endDate': '2019-06-29'}, 'value': '53809000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-12-30', 'endDate': '2019-03-30'}, 'value': '58015000000'}
{'decimals': '-6', 'unitRef': 'usd', 'period': {'startDate': '2018-09-30', 'endDate': '2018-12-29'}, 'value': '84310000000'}

In the next step, we extract XBRL data from Apple's 10-Q filing and convert it into a pandas DataFrame allowing us to access information such as total revenue per quarter, revenue per product, such as Mac or iPad, and revenue per region, such as America, Europe, Greater China, Japan and more.

aapl_10k = "https://www.sec.gov/Archives/edgar/data/320193/000032019323000006/aapl-20221231.htm"
aapl_xbrl = xbrlApi.xbrl_to_json(htm_url=aapl_10k)

import pandas as pd 

revenue = pd.json_normalize(aapl_xbrl["StatementsOfIncome"]["RevenueFromContractWithCustomerExcludingAssessedTax"])
revenue

	decimals	unitRef	value	period.startDate	period.endDate	segment.dimension	segment.value
0	-6	usd	96388000000	2022-09-25	2022-12-31	srt:ProductOrServiceAxis	us-gaap:ProductMember
1	-6	usd	104429000000	2021-09-26	2021-12-25	srt:ProductOrServiceAxis	us-gaap:ProductMember
2	-6	usd	20766000000	2022-09-25	2022-12-31	srt:ProductOrServiceAxis	us-gaap:ServiceMember
3	-6	usd	19516000000	2021-09-26	2021-12-25	srt:ProductOrServiceAxis	us-gaap:ServiceMember
4	-6	usd	117154000000	2022-09-25	2022-12-31	NaN	NaN
5	-6	usd	123945000000	2021-09-26	2021-12-25	NaN	NaN
6	-6	usd	65775000000	2022-09-25	2022-12-31	srt:ProductOrServiceAxis	aapl:IPhoneMember
7	-6	usd	71628000000	2021-09-26	2021-12-25	srt:ProductOrServiceAxis	aapl:IPhoneMember
8	-6	usd	7735000000	2022-09-25	2022-12-31	srt:ProductOrServiceAxis	aapl:MacMember
9	-6	usd	10852000000	2021-09-26	2021-12-25	srt:ProductOrServiceAxis	aapl:MacMember
10	-6	usd	9396000000	2022-09-25	2022-12-31	srt:ProductOrServiceAxis	aapl:IPadMember
11	-6	usd	7248000000	2021-09-26	2021-12-25	srt:ProductOrServiceAxis	aapl:IPadMember
12	-6	usd	13482000000	2022-09-25	2022-12-31	srt:ProductOrServiceAxis	aapl:WearablesHomeandAccessoriesMember
13	-6	usd	14701000000	2021-09-26	2021-12-25	srt:ProductOrServiceAxis	aapl:WearablesHomeandAccessoriesMember
14	-6	usd	49278000000	2022-09-25	2022-12-31	us-gaap:StatementBusinessSegmentsAxis	aapl:AmericasSegmentMember
15	-6	usd	51496000000	2021-09-26	2021-12-25	us-gaap:StatementBusinessSegmentsAxis	aapl:AmericasSegmentMember
16	-6	usd	27681000000	2022-09-25	2022-12-31	us-gaap:StatementBusinessSegmentsAxis	aapl:EuropeSegmentMember
17	-6	usd	29749000000	2021-09-26	2021-12-25	us-gaap:StatementBusinessSegmentsAxis	aapl:EuropeSegmentMember
18	-6	usd	23905000000	2022-09-25	2022-12-31	us-gaap:StatementBusinessSegmentsAxis	aapl:GreaterChinaSegmentMember
19	-6	usd	25783000000	2021-09-26	2021-12-25	us-gaap:StatementBusinessSegmentsAxis	aapl:GreaterChinaSegmentMember
20	-6	usd	6755000000	2022-09-25	2022-12-31	us-gaap:StatementBusinessSegmentsAxis	aapl:JapanSegmentMember
21	-6	usd	7107000000	2021-09-26	2021-12-25	us-gaap:StatementBusinessSegmentsAxis	aapl:JapanSegmentMember
22	-6	usd	9535000000	2022-09-25	2022-12-31	us-gaap:StatementBusinessSegmentsAxis	aapl:RestOfAsiaPacificSegmentMember
23	-6	usd	9810000000	2021-09-26	2021-12-25	us-gaap:StatementBusinessSegmentsAxis	aapl:RestOfAsiaPacificSegmentMember

The next example shows the function get_income_statement() that extracts the income statement from the XBRL data of the 10-Q SEC filing and converts the statement into a pandas DataFrame.

# convert XBRL-JSON of income statement to pandas dataframe
def get_income_statement(xbrl_json):
    income_statement_store = {}

    # iterate over each US GAAP item in the income statement
    for usGaapItem in xbrl_json['StatementsOfIncome']:
        values = []
        indicies = []

        for fact in xbrl_json['StatementsOfIncome'][usGaapItem]:
            # only consider items without segment. not required for our analysis.
            if 'segment' not in fact:
                index = fact['period']['startDate'] + '-' + fact['period']['endDate']
                # ensure no index duplicates are created
                if index not in indicies:
                    values.append(fact['value'])
                    indicies.append(index)                    

        income_statement_store[usGaapItem] = pd.Series(values, index=indicies) 

    income_statement = pd.DataFrame(income_statement_store)
    # switch columns and rows so that US GAAP items are rows and each column header represents a date range
    return income_statement.T 

income_statement = get_income_statement(aapl_xbrl)
income_statement

	2022-09-25-2022-12-31	2021-09-26-2021-12-25
RevenueFromContractWithCustomerExcludingAssessedTax	117154000000	123945000000
CostOfGoodsAndServicesSold	66822000000	69702000000
GrossProfit	50332000000	54243000000
ResearchAndDevelopmentExpense	7709000000	6306000000
SellingGeneralAndAdministrativeExpense	6607000000	6449000000
OperatingExpenses	14316000000	12755000000
OperatingIncomeLoss	36016000000	41488000000
NonoperatingIncomeExpense	-393000000	-247000000
IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest	35623000000	41241000000
IncomeTaxExpenseBenefit	5625000000	6611000000
NetIncomeLoss	29998000000	34630000000
EarningsPerShareBasic	1.89	2.11
EarningsPerShareDiluted	1.88	2.10
WeightedAverageNumberOfSharesOutstandingBasic	15892723000	16391724000
WeightedAverageNumberOfDilutedSharesOutstanding	15955718000	16519291000

The next example illustrates how to extract the income statement from the XBRL data of an SEC filing, such as a 10-Q or 10-K report, and export the statement to an Excel file.

income_statement.to_excel('income-statement.xlsx')

The resulting Excel file income-statement.xlsx looks like this.

The following function get_balance_sheet() extracts the balance sheet from the XBRL data of the 10-Q SEC filing and converts it into a pandas DataFrame.

# convert XBRL-JSON of balance sheet to pandas dataframe
def get_balance_sheet(xbrl_json):
    balance_sheet_store = {}

    for usGaapItem in xbrl_json['BalanceSheets']:
        values = []
        indicies = []

        for fact in xbrl_json['BalanceSheets'][usGaapItem]:
            # only consider items without segment.
            if 'segment' not in fact:
                index = fact['period']['instant']

                # avoid duplicate indicies with same values
                if index in indicies:
                    continue
                    
                # add 0 if value is nil
                if "value" not in fact:
                    values.append(0)
                else:
                    values.append(fact['value'])

                indicies.append(index)                    

            balance_sheet_store[usGaapItem] = pd.Series(values, index=indicies) 

    balance_sheet = pd.DataFrame(balance_sheet_store)
    # switch columns and rows so that US GAAP items are rows and each column header represents a date instant
    return balance_sheet.T

balance_sheet = get_balance_sheet(aapl_xbrl)
balance_sheet

	2021-09-25	2021-12-25	2022-09-24	2022-12-31
CashAndCashEquivalentsAtCarryingValue	NaN	NaN	23646000000	20535000000
MarketableSecuritiesCurrent	NaN	NaN	24658000000	30820000000
AccountsReceivableNetCurrent	NaN	NaN	28184000000	23752000000
InventoryNet	NaN	NaN	4946000000	6820000000
NontradeReceivablesCurrent	NaN	NaN	32748000000	30428000000
OtherAssetsCurrent	NaN	NaN	21223000000	16422000000
AssetsCurrent	NaN	NaN	135405000000	128777000000
MarketableSecuritiesNoncurrent	NaN	NaN	120805000000	114095000000
PropertyPlantAndEquipmentNet	NaN	NaN	42117000000	42951000000
OtherAssetsNoncurrent	NaN	NaN	54428000000	60924000000
AssetsNoncurrent	NaN	NaN	217350000000	217970000000
Assets	NaN	NaN	352755000000	346747000000
AccountsPayableCurrent	NaN	NaN	64115000000	57918000000
OtherLiabilitiesCurrent	NaN	NaN	60845000000	59893000000
ContractWithCustomerLiabilityCurrent	NaN	NaN	7912000000	7992000000
CommercialPaper	NaN	NaN	9982000000	1743000000
LongTermDebtCurrent	NaN	NaN	11128000000	9740000000
LiabilitiesCurrent	NaN	NaN	153982000000	137286000000
LongTermDebtNoncurrent	NaN	NaN	98959000000	99627000000
OtherLiabilitiesNoncurrent	NaN	NaN	49142000000	53107000000
LiabilitiesNoncurrent	NaN	NaN	148101000000	152734000000
Liabilities	NaN	NaN	302083000000	290020000000
CommitmentsAndContingencies	NaN	NaN	0.0	0.0
CommonStocksIncludingAdditionalPaidInCapital	NaN	NaN	64849000000	66399000000
RetainedEarningsAccumulatedDeficit	NaN	NaN	-3068000000	3240000000
AccumulatedOtherComprehensiveIncomeLossNetOfTax	NaN	NaN	-11109000000	-12912000000
StockholdersEquity	63090000000	71932000000	50672000000	56727000000
LiabilitiesAndStockholdersEquity	NaN	NaN	352755000000	346747000000

The next example illustrates how to extract the address of a company or filer from the cover page of an XBRL instance document attached to an SEC filing. For this example, we use one of Google's 10-K filings.

The XBRL cover page includes information such as:

• Company's fiscal year end
• Trading symbols and exchanges
• Tax identification number
• Phone number
• Business and filer category, e.g. large accelerated filer or emerging growth company.

goog_10k = "https://www.sec.gov/Archives/edgar/data/1652044/000165204423000016/goog-20221231.htm"
goog_xbrl = xbrlApi.xbrl_to_json(htm_url=goog_10k)

print("Cover page items")
print('---------------------------------')
print(*list(goog_xbrl["CoverPage"]), sep='\n')

Cover page items
---------------------------------
DocumentType
DocumentAnnualReport
DocumentPeriodEndDate
CurrentFiscalYearEndDate
DocumentTransitionReport
EntityFileNumber
EntityRegistrantName
EntityIncorporationStateCountryCode
EntityTaxIdentificationNumber
EntityAddressAddressLine1
EntityAddressCityOrTown
EntityAddressStateOrProvince
EntityAddressPostalZipCode
CityAreaCode
LocalPhoneNumber
Security12bTitle
TradingSymbol
SecurityExchangeName
EntityWellKnownSeasonedIssuer
EntityVoluntaryFilers
EntityCurrentReportingStatus
EntityInteractiveDataCurrent
EntityFilerCategory
EntitySmallBusiness
EntityEmergingGrowthCompany
IcfrAuditorAttestationFlag
EntityShellCompany
EntityPublicFloat
EntityCommonStockSharesOutstanding
DocumentsIncorporatedByReferenceTextBlock
AmendmentFlag
DocumentFiscalYearFocus
DocumentFiscalPeriodFocus
EntityCentralIndexKey

---

We identify five relevant keys in the cover page that include information about the company's address:

• EntityAddressAddressLine1
• EntityAddressCityOrTown
• EntityAddressStateOrProvince
• EntityAddressPostalZipCode
• CityAreaCode

cover = goog_xbrl["CoverPage"]

print("Google's address:")
print(cover["EntityAddressAddressLine1"])
print(cover["EntityAddressCityOrTown"])
print(cover["EntityAddressStateOrProvince"])
print(cover["EntityAddressPostalZipCode"])
print(cover["CityAreaCode"])

Google's address:
1600 Amphitheatre Parkway
Mountain View
CA
94043
650