The Bureau of Economic Analysis (BEA) publishes economic statistics in a variety of formats. This document describes the BEA Data Retrieval Application Programming Interface (API) – including detailed instructions for retrieving data and meta-data published by BEA using the pyBEA package.
The pyBEA pacakge provides a simple interface to the BEA API and includes methods for retrieving a subset of BEA statistical data, including any meta-data describing it, and loading the results into a Pandas DataFrame object for further analysis.
The BEA API returns data in one of two formats: JSON or XML (with JSON being the default). Currently the pyBEA package only supports JSON requests.
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import pybea
The BEA API contains three methods for retrieving meta-data as follows:
GetDataSetList: retrieves a list of the datasets currently offered.GetParameterList: retrieves a list of the parameters (required and optional) for a particular dataset.GetParameterValues: retrieves a list of the valid values for a particular parameter.Each of these methods has a corresponding function in the pybea package.
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pybea.get_data_set_list?
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pybea.get_parameter_list?
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pybea.get_parameter_values?
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# replace this with your BEA data API key!
USER_ID = ???
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# access the BEA data API...
available_datasets = pybea.get_data_set_list(USER_ID)
available_datasets
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request = pybea.api.DataSetListRequest(USER_ID,
ResultFormat="JSON")
request.data_set_list
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regional_income_params = pybea.get_parameter_list(USER_ID,
DataSetName='RegionalIncome',
ResultFormat="XML")
regional_income_params
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request = pybea.api.ParameterListRequest(USER_ID,
DataSetName='RegionalIncome',
ResultFormat="JSON")
request.parameter_list
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regional_income_geofips = pybea.get_parameter_values(USER_ID,
DataSetName='RegionalIncome',
ParameterName='GeoFips')
regional_income_geofips
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request = pybea.api.ParameterValuesRequest(USER_ID,
DataSetName='RegionalIncome',
ParameterName='GeoFips')
request.parameter_values
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pybea.get_data?
This dataset contains data from the National Income and Product Accounts which include measures of the value and composition of U.S.production and the incomes generated in producing it. NIPA data is provided on a table basis; individual tables contain between fewer than 10 to more than 200 distinct data series.
Percent change in Real Gross Domestic Product, Annually and Quarterly for all years.
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data = pybea.get_data(USER_ID,
DataSetName='NIPA',
TableName='T10101',
Frequency=['A', 'Q'],
Year='ALL',
ResultFormat="XML"
)
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data.head()
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data.tail()
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request = pybea.api.NIPARequest(USER_ID,
TableName='T20600',
Frequency='M',
Year=['2015', '2016'],
ResultFormat='JSON')
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request.data.head()
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data.tail()
The DataSetName is NIUnderlyingDetail. This dataset contains underlying detail data from the National Income and Product Accounts which include measures of the value and composition of U.S.production and the incomes generated in producing it. NIPA Underlying Detail data is provided on a table basis; individual tables contain between fewer than 10 to more than 200 distinct data series.
Personal Consumption Expenditures, Current Dollars, Annually, Quarterly and Monthly for all years.
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data = pybea.get_data(USER_ID,
DataSetName='NIUnderlyingDetail',
TableName='U20305',
Frequency=['A', 'Q'],
Year='ALL',
ResultFormat='XML')
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data.head()
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data.tail()
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request = pybea.api.NIUnderlyingDetailRequest(USER_ID,
TableName='U70205S',
Frequency='M',
Year=['2015', '2016'],
ResultFormat='JSON')
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request.data.head()
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request.data.tail()
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data = pybea.get_data(USER_ID,
DataSetName='FixedAssets',
TableID='16',
Year='2012',
ResultFormat='XML')
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data.head()
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data.tail()
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data = pybea.get_data(USER_ID,
DataSetName='ITA',
Indicator='BalGds',
AreaOrCountry='China',
Frequency='A',
Year=['2011', '2012'],
ResultFormat='XML')
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data.head()
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data = pybea.get_data(USER_ID,
DataSetName='ITA',
Indicator='PfInvAssets',
AreaOrCountry='AllCountries',
Frequency='QNSA',
Year='2013',
ResultFormat='XML')
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data.head()
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data = pybea.get_data(USER_ID,
DataSetName='RegionalIncome',
TableName='CA1',
LineCode=1,
GeoFips='COUNTY',
Year=['2012', '2013'],
ResultFormat='JSON')
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data.head()
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data.tail()
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data = pybea.get_data(USER_ID,
DataSetName='RegionalProduct',
Component="RGDP_MAN",
IndustryId=1,
GeoFips="MSA",
Year="ALL",
ResultFormat='XML')
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data.head()
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data.tail()
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southeast_states = ["01000", "05000", "12000", "13000", "21000", "22000",
"28000", "37000", "45000", "47000", "51000", "54000"]
data = pybea.get_data(USER_ID,
DataSetName='RegionalProduct',
Component="GDP_sAN",
IndustryId=35,
GeoFips=southeast_states,
Year=["2013", "2013"],
ResultFormat='XML')
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data.head()
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data.tail()
The Input-Output Statistics are contained within a dataset called InputOutput. BEA's industry accounts are used extensively by policymakers and businesses to understand industry interactions, productivity trends, and the changing structure of the U.S. economy. The input-output accounts provide a detailed view of the interrelationships between U.S. producers and users.
Data from The Use of Commodities by Industries, Before Redefinitions (Producer’s Prices) sector level table for years 2010, 2011, and 2012.
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data = pybea.get_data(USER_ID,
DataSetName='InputOutput',
TableID=2,
Year=['2010', '2011', '2012', '2013'],
ResultFormat='JSON')
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data.head()
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data.tail()
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data = pybea.get_data(USER_ID,
DataSetName='InputOutput',
TableID=[46, 47],
Year='2007',
ResultFormat='JSON')
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data.head()
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data.tail()
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