finds.readers.bea

Wrapper over BEA web api and data source files

  • Bureau of Economic Analysis: Input-Output Use Tables

BEA released initial results of the comprehensive update of the National Economic Accounts (NEAs), which include the National Income and Product Accounts (NIPAs) and the Industry Economic Accounts (IEAs), on September 28, 2023.

Copyright 2022, Terence Lim

MIT License

class finds.readers.bea.BEA(rdb: RedisDB | None, userid: str, verbose: int = 1)[source]

Bases: object

Class and methods to retrieve BEA web api and IOUse datasets

Parameters:

  • rdb – Redis connection instance to cache data from BEA web site

  • userid – Register with BEA to get key for their web api

Variables:

  • short_desc – Reference Series maps BEA industry to custom descriptions

  • params – Some common parameter sets for BEA web api

Examples:

>>> bea = BEA()
>>> df = bea.get(**BEA.items['industries'])
>>> sql.load_dataframe('gdpindustry', df, index_label=None, if_exists='replace')
>>> df = bea.get(**BEA.items['ioUse'], year=2018)
get(datasetname: str = '', parametername: str = '', cache_mode: str = 'rw', **kwargs) → DataFrame[source]

Execute common BEA web api calls

Parameters:

  • datasetname – Name of dataset to retrieve, e.g. ‘ioUse’

  • parametername – Parameter to retrieve, e.g. ‘TableID’

  • cache_mode – ‘r’ to try read from cache first, ‘w’ to write to cache

  • kwargs – Additional parameters, such as tableid or year

Examples:

>>> datasetname='ioUse'
>>> tableid=259
>>> year = 2017
>>> parametername = 'TableID'
>>> bea.get()
>>> bea.get(datasetname)
>>> bea.get(datasetname, parametername=parametername)
>>> bea.get('ioUse')
>>> df = bea.get(datasetname='ioUse', tableid=259, year=2018)
>>> df = bea.get(datasetname='GDPbyIndustry', parametername='Industry')
read_ioUse(year: int, vintage: int = 0, cache_mode: str = 'rw') → DataFrame[source]

Load ioUse table from BEA web api (or xls if early vintage)

Parameters:

  • year – Year of IO-Use to load

  • vintage – Year of sectoring; allows different eras to be compared

  • cache_mode – ‘r’ to try read from cache first, ‘w’ to write to cache

Returns:

DataFrame in stacked form, flows amounts in ‘datavalue’ column, and columns ‘rowcode’, ‘colcode’ label maker and user industry

Notes:

  • rows, column codes that start with (‘T’,’U’,’V’,’Other’) are dropped

  • ‘F’: final_use,

  • ‘G’: govt,

  • ‘T’: total&tax,

  • ‘U’: used,

  • ‘V’:value_added,

  • ‘O’:imports

  • generally, should drop = (‘F’,’T’,’U’,’V’,’Other’)

Examples:

>>> bea=BEA()
>>> data = bea.read_ioUse(1996)
>>> sql.load_dataframe('ioUse', data, index_label=None, if_exists='replace')
>>> data = sql.select('ioUse', where={'year': 2017, 'tableid' : 259})
>>> df = data.pivot(index='rowcode', columns='colcode', values='datavalue')
read_ioUse_xls(year: int, cache_mode: str = 'rw', source: str | None = None) → DataFrame[source]

Helper to load a year’s ioUSE table from vintage xls on website

Parameters:

  • year – year of IoUse to fetch

  • cache_mode – ‘r’ to try read from cache first, ‘w’ to write to cache

  • source – url or filename to read from

static sectoring(year: int, source: str = '')[source]

Returns BEA sector definitions, based on naics, from xls on BEA website

Parameters:

  • year – Year of historical BEA scheme, in {1997, 1964, 1947}

  • source – Source url or local file

Notes:

params = {'gdpindustry': {'datasetname': 'GDPbyIndustry', 'index': 'key', 'parametername': 'Industry'}, 'ioUse': {'datasetname': 'inputoutput', 'tableid': 259}}
short_desc = 11                   Agriculture 111CA                      Farms 113FF           Forestry,fishing 21                        Mining 211                     Oil, gas                   ...            GFGN                  Nondefense GFE          Federal enterprises GSL                  State local GSLG         State local general GSLE     State local enterprises Name: label, Length: 95, dtype: object