beaapi

beaapi: A Python library library to make it easier to retrieve and work with BEA data. For the parallel R-package, see bea.R.

To install the package, download the .whl file for the latest release, and install using pip.

For help, see our documentation.

For development and building information, see CONTRIBUTING.md.

To Get Started

Once you have installed the package, you can load it in your Python script as follows:

import beaapi

To use the package, you must first register for an API key from BEA by providing your name and email address. The key will be emailed to you.

Once you have received your BEA API key, you can save it to a variable to make it easier to use later:

beakey = 'YOUR 36-DIGIT API KEY'

For scripts, an even better method is to create an unversioned text file called .env with the contents beakey=Your36DigitAPiKey and then use the package python-dotenv to load it automatically.

import os
from dotenv import load_dotenv
load_dotenv()
beakey = os.environ.get("beakey")

Get metadata

The BEA data is distributed across various datasets that each contain many tables. To get a list of the datasets available on the API, use get_data_set_list():

list_of_sets = beaapi.get_data_set_list(beakey)
display(list_of_sets)  # Note the last dataset is only for speeding up metadata queries

	DatasetName	DatasetDescription
0	NIPA	Standard NIPA tables
1	NIUnderlyingDetail	Standard NI underlying detail tables
2	MNE	Multinational Enterprises
3	FixedAssets	Standard Fixed Assets tables
4	ITA	International Transactions Accounts
5	IIP	International Investment Position
6	InputOutput	Input-Output Data
7	IntlServTrade	International Services Trade
8	GDPbyIndustry	GDP by Industry
9	Regional	Regional data sets
10	UnderlyingGDPbyIndustry	Underlying GDP by Industry
11	APIDatasetMetaData	Metadata about other API datasets

Queries to the different datasets take different parameters. To get a list of the paramaters for a given dataset, use get_parameter_list(); for example, to get a list of the parameters for the NIPA dataset, use:

list_of_params = beaapi.get_parameter_list(beakey, 'NIPA')
display(list_of_params)

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	Frequency	string	A - Annual, Q-Quarterly, M-Monthly	1		1
1	ShowMillions	string	A flag indicating that million-dollar data sho...	0	N	0
2	TableID	integer	The standard NIPA table identifier	0	<NA>	0
3	TableName	string	The new NIPA table identifier	0	<NA>	0
4	Year	integer	List of year(s) of data to retrieve (X for All)	1		1	X

To get a list of the values for a given parameter, use get_parameter_values(); for example, to get a list of the parameter values for the Frequency parameter of the NIPA dataset, use:

list_of_param_vals = beaapi.get_parameter_values(beakey, 'NIPA', 'Frequency')
display(list_of_param_vals)

	FrequencyID	Description
0	A	Annual
1	Q	Quarterly
2	M	Monthly

The API documentation is a good place to also understand allowed values.

A few datasets (ITA, IIP, InputOutput, IntlServTrade, GDPbyIndustry, Regional, and UnderlyingGDPbyIndustry) allow you to filter the values based on what will be passed in for other parameters.

tbl = beaapi.get_parameter_values_filtered(beakey, 'ITA', targetparameter='Indicator', AreaOrCountry="China",Frequency="A", Year="2011")
display(tbl.head())

	Key	Desc
0	BalCapAcct	Balance on capital account
1	BalCurrAcct	Balance on current account
2	BalGds	Balance on goods
3	BalGdsServ	Balance on goods and services
4	BalPrimInc	Balance on primary income

A few of the datasets publish metadata tables that can be queried for particular strings using search_metadata(). This method allows you to search for BEA data by keyword. For example, to find all datasets in which the term “personal consumption” appears, use the following:

search_data = beaapi.search_metadata('Gross domestic', beakey)
search_data.head(2)

	SeriesCode	RowNumber	LineDescription	LineNumber	ParentLineNumber	Tier	Path	TableId	Datasetname	TableName	ReleaseDate	NextReleaseDate
0	A191RL	10	Gross domestic product	1		0	1	T10101	NIPA	Table 1.1.1. Percent Change From Preceding Per...	Feb 28 2019 8:30AM	Mar 28 2019 8:30AM
26	A191RP	280	Gross domestic product, current dollars	27		0	27	T10101	NIPA	Table 1.1.1. Percent Change From Preceding Per...	Feb 28 2019 8:30AM	Mar 28 2019 8:30AM

Please note that that search_metadata currently searches only national data.

The contents of this function are automatically updated using a new metadata component of BEA’s API; as such, we recommend that you use it with your API key, and the first use of this function requires that you use your key or it will be unable to extract the metadata.

If you do not wish to automatically update the metadata (e.g., you have conducted a study using the search function), simply searching for the term without also passing your key to the function will do a search only using your locally stored version.

Get data

Once you have identified the TableId number and other information, you can use get_data() to access the data. The following code, for example, returns the NIPA table with 2015 data for Table T20305.

bea_tbl = beaapi.get_data(beakey, datasetname='NIPA', TableName='T20305', Frequency='Q', Year='2015')
display(bea_tbl.head(2))

	TableName	SeriesCode	LineNumber	LineDescription	TimePeriod	METRIC_NAME	CL_UNIT	UNIT_MULT	DataValue	NoteRef
0	T20305	DPCERC	1	Personal consumption expenditures (PCE)	2015Q1	Current Dollars	Level	6	12083904	T20305
1	T20305	DPCERC	1	Personal consumption expenditures (PCE)	2015Q2	Current Dollars	Level	6	12224707	T20305

We store in the meta-data the index columns so that you can create a unique index on the data-frame.

display(bea_tbl.set_index(bea_tbl.attrs['index_cols']).head(2))

		TableName	SeriesCode	LineDescription	METRIC_NAME	CL_UNIT	UNIT_MULT	DataValue	NoteRef
LineNumber	TimePeriod
1	2015Q1	T20305	DPCERC	Personal consumption expenditures (PCE)	Current Dollars	Level	6	12083904	T20305
	2015Q2	T20305	DPCERC	Personal consumption expenditures (PCE)	Current Dollars	Level	6	12224707	T20305

Extra meta-data from the API is returned in a dictionary in the attributes called detail and can vary based on the dataset.

print('Extra detail keys:' + str(bea_tbl.attrs['detail'].keys()))
print("Let's look at some interesting ones.")
print('Statistic: ' + bea_tbl.attrs['detail']['Statistic'])
print("Notes corresponding to NoteRef:")
display(bea_tbl.attrs['detail']['Notes'].head())

Extra detail keys:dict_keys(['Statistic', 'UTCProductionTime', 'Dimensions', 'Notes'])
Let's look at some interesting ones.
Statistic: NIPA Table
Notes corresponding to NoteRef:

	NoteRef	NoteText
0	T20305	Table 2.3.5. Personal Consumption Expenditures...
1	T20305.1	1. Net expenses of NPISHs, defined as their gr...
2	T20305.2	2. Gross output is net of unrelated sales, sec...
3	T20305.3	3. Excludes unrelated sales, secondary sales, ...
4	T20305.4	4. Food consists of food and beverages purchas...

To retrieve a limited selection of multiple years, list all the years you want to retrieve. For example, to retrieve data for 2011-2015, use Year="2011,2012,2013,2014,2015"

The API documentation includes information about the specific parameters required by get_data().

If you would like to format the data in a wide format so that different variables (in this case LineNumbers) are different rows, you can use to_wide_vars_to_cols().

display(beaapi.to_wide_vars_in_cols(bea_tbl).head(3))

LineNumber	1	2	3	4	5	6	7	8	9	10	...	19	20	21	22	23	24	25	26	27	28
TimePeriod
2015Q1	12083904	3867908	1283823	465426	285887	332965	199546	2584085	917371	366171	...	814298	960760	992461	362114	1367850	1005736	10619844	546689	10570891	9107467
2015Q2	12224707	3927357	1309627	481820	292631	333600	201577	2617730	916930	368315	...	828712	977198	1003757	363147	1374002	1010855	10759482	548295	10693235	9228648
2015Q3	12347752	3960436	1318493	481620	297306	337083	202483	2641944	924494	370385	...	836130	978430	1012278	373825	1385653	1011828	10877903	545354	10802962	9333737

3 rows × 28 columns

Advanced topics

Throttling: The BEA api limits requests to a maximum of 100/minute and 100MB/minute (as well as 30 errors/minute). If the user exceeds this, they will be denied access for 1 hour. This package will automatically self-throttle, so in general, the user does not have to worry about that.

See the docs for additional information on: - How to construct API queries to pull data from existing iTables.

Quick Links

• CODE_OF_CONDUCT.md

• SECURITY

• LICENSE

• CONTRIBUTING.md

• SUPPORT.md

• CHANGELOG.md

Disclaimer

The United States Department of Commerce (DOC) GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal law. Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.

Use of this library may result in data being stored on users’ local machines. Specifically, local copies of BEA API metadata may be stored and automatically updated in the beaapi_data directory (unless otherwise changed) in order to improve performance of search_metadata().