How to translate iTable tables to API queries¶

This guide shows how to translate the process of getting data from various iTables searches to using the API. We do this for each of the datasets.

General info¶

Note that for some scenarios it may take several API queries to generate the data for a single iTables table. Also, in the examples below we often search for a target parameter by a string value in a table. You can always export the table of possibilities to spreadsheet to view the whole thing (e.g., tbl.to_csv("fname.csv", index=False))

We first need to figure out what dataset to pull from. Here’s roughly how the “tabs” on the iTables web page map to the different API datasets:

“(National Data) GDP & Personal Income” -> [NIPA, NIUnderlyingDetail]
“Fixed Assets” -> FixedAssets
“GDP by Industry” -> [GDPbyIndustry, UnderlyingGDPbyIndustry]
“Input-Output” -> InputOutput
“Int’l Transactions, Services, & IIP” -> [IntlServTrade, ITA, IIP]
“Direct Investments & MNEs” -> MNE
“(Regional Data) GDP & Personal Income” -> Regional

Other resources¶

While we show one method here, there are some other resources that may be helpful:

search_metadata() can be be used for ‘FixedAssets’, ‘NIPA’, ‘NIUnderlyingDetail’.
NIPA web resources: Table Title to TableID, Series Details to Series Code, NIPA keywords
Fixed Assets web resources: Table Register, Series Register,
Regional API PDF has table mapping

Setup¶

[1]:

import beaapi

import pandas as pd
pd.set_option('display.max_colwidth', None)  # show all text in cells

[2]:

# Get key from unversioned file
import os
from dotenv import load_dotenv
load_dotenv()
beakey = os.environ.get("beakey")

Code utilities¶

We define here a few tools to be use below

[9]:

# Get some basic Meta-data. What are the table names?
datasets_info = beaapi.get_data_set_list(beakey)
dataset_names = list(datasets_info['DatasetName'])
param_infos = {dataset_name: beaapi.get_parameter_list(beakey, dataset_name) for dataset_name in dataset_names}

Make a function get_table_param_from_desc() to ease getting table IDs from descriptions

[10]:

# Tools to help lookup table IDs from table descriptions
table_var = {'NIPA':'TableName',
    'NIUnderlyingDetail':'TableName',
    'MNE':None,
    'FixedAssets': 'TableName',
    'ITA':None,
    'IIP':None,
    'InputOutput':"TableID",
    'IntlServTrade':None,
    'GDPbyIndustry':"TableID",
    'Regional': "TableName",
    'UnderlyingGDPbyIndustry':"TableID"}
table_param_desc = {'NIPA':'Description',
    'NIUnderlyingDetail':'Description',
    'MNE':None,
    'FixedAssets': 'Description',
    'ITA':None,
    'IIP':None,
    'InputOutput':"Desc",
    'IntlServTrade':None,
    'GDPbyIndustry':"Desc",
    'Regional': "Desc",
    'UnderlyingGDPbyIndustry':"Desc"}
table_names = {}
for dataset_name, table_var_name in table_var.items():
    if table_var_name is not None:
        table_names[dataset_name] = beaapi.get_parameter_values(beakey, dataset_name, table_var_name)

def get_table_param_from_desc(dataset_name, desc):
    mask = table_names[dataset_name][table_param_desc[dataset_name]].str.contains(desc)
    results = table_names[dataset_name][mask]
    table_var_name = table_var[dataset_name]
    return table_var_name, results

How to build query from iTables¶

National Data - GDP & Personal Income¶

NIPA (publication category “GDP and Personal Income”, default)¶

First, let’s take as example table the first one on the web page and describe what we do have

[39]:

# Example
desc = "Table 1.1.1. Percent Change From Preceding Period in Real Gross Domestic Product"
freq = "Q"
year = "2019" # input as string

Then let’s see what we need to build the query

[40]:

display(param_infos["NIPA"])

	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

Note, we actually need exactly 1 of TableID or TableName. (They transitioned to TableName, but keep TableID around for compatibility.)

Next let’s find the TableName from the description.

[41]:

table_param_name, table_param_vals = get_table_param_from_desc("NIPA", desc)
display(table_param_vals)
print("We'll use that one")
table_param_val = table_param_vals.iloc[0,0]

	TableName	Description
0	T10101	Table 1.1.1. Percent Change From Preceding Per...

We'll use that one

Finally, let’s get the data.

[42]:

tbl = beaapi.get_data(beakey, "NIPA", TableName=table_param_val, Frequency=freq, Year=year)
tbl.head()

[42]:

	TableName	SeriesCode	LineNumber	LineDescription	TimePeriod	METRIC_NAME	CL_UNIT	DataValue	NoteRef
0	T10101	A191RL	1	Gross domestic product	2019Q1	Fisher Quantity Index	Percent change, annual rate	2.4	T10101
1	T10101	A191RL	1	Gross domestic product	2019Q2	Fisher Quantity Index	Percent change, annual rate	3.2	T10101
2	T10101	A191RL	1	Gross domestic product	2019Q3	Fisher Quantity Index	Percent change, annual rate	2.8	T10101
3	T10101	A191RL	1	Gross domestic product	2019Q4	Fisher Quantity Index	Percent change, annual rate	1.9	T10101
4	T10101	DPCERL	2	Personal consumption expenditures	2019Q1	Fisher Quantity Index	Percent change, annual rate	0.6	T10101

NIUnderlyingDetail (Publication Category “Underlying Details”, non-default)¶

[12]:

# Info from iTables
desc = "Table 1AU. Real Manufacturing and Trade Inventories, Seasonally Adjusted, End of Period" # Note: have to cut off final "[Chained 1996 dollars, 1967-96, SIC]" to get it to match
freq="Q"
year="1995"

print("What parameters are needed")
display(param_infos["NIUnderlyingDetail"])

What parameters are needed

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	Frequency	string	A - Annual, Q-Quarterly, M-Monthly	1		1
1	TableID	integer	The standard NI underlying detail table identi...	0	<NA>	0
2	TableName	string	The new NIPA table identifier	0	<NA>	0
3	Year	integer	List of year(s) of data to retrieve (X for All)	1		1	X

Get tablename from iTable’s table description from web

[13]:

table_param_name, table_param_vals = get_table_param_from_desc("NIUnderlyingDetail", desc)
display(table_param_vals)
print("That one looks good.")
table_param_val = table_param_vals.iloc[0,0]

What TableName matches?

	TableName	Description
0	U001A	Table 1AU. Real Manufacturing and Trade Invent...

Next, get data.

[14]:

tbl = beaapi.get_data(beakey, "NIUnderlyingDetail", TableName=table_param_val, Frequency=freq, Year=year)
tbl.head()

[14]:

	TableName	SeriesCode	LineNumber	LineDescription	TimePeriod	METRIC_NAME	CL_UNIT	UNIT_MULT	DataValue	NoteRef
0	U001A	KSTMT	1	Manufacturing and trade	1995Q1	Chained Dollars	Level	6	973434	U001A
1	U001A	KSTMT	1	Manufacturing and trade	1995Q2	Chained Dollars	Level	6	984218	U001A
2	U001A	KSTMT	1	Manufacturing and trade	1995Q3	Chained Dollars	Level	6	991162	U001A
3	U001A	KSTMT	1	Manufacturing and trade	1995Q4	Chained Dollars	Level	6	994259	U001A
4	U001A	KSTM	2	Manufacturing	1995Q1	Chained Dollars	Level	6	410079	U001A

[10]:

# Unsure how to access Publication category "Additional Tables"
# desc="FWPI-1. Fixed-Weighted Price Indexes"
# Both methods fail:
#search_result = beaapi.search_metadata(desc, beakey)
#display(search_result)
#table_param_name, table_param_vals = get_table_param_from_desc("NIPA", desc) #Tried with NIUnderlyingDetail
#display(table_param_vals)

National Data - Fixed Assets¶

FixedAssets¶

[22]:

# Info from iTable
desc = "Table 1.1. Current-Cost Net Stock of Fixed Assets and Consumer Durable Goods"
year="2013"

First, what parameters are needed?

[18]:

display(param_infos["FixedAssets"])

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	TableName	string	The new Fixed Assets identifier	1		0
1	Year	integer	List of year(s) of data to retrieve (X for All)	1		1	X

Get tablename from iTable’s table description from web

[24]:

table_param_name, table_param_vals = get_table_param_from_desc("FixedAssets", desc)
display(table_param_vals)
print("That one looks good")
table_param_val = table_param_vals.iloc[0,0]

	TableName	Description
0	FAAt101	Table 1.1. Current-Cost Net Stock of Fixed Ass...

Get data

[25]:

tbl = beaapi.get_data(beakey, "FixedAssets", TableName=table_param_val, Year=year)
tbl.head()

[25]:

	TableName	SeriesCode	LineNumber	LineDescription	TimePeriod	METRIC_NAME	CL_UNIT	UNIT_MULT	DataValue	NoteRef
0	FAAt101	k1wtotl1es00	1	Fixed assets and consumer durable goods	2013	Current Dollars	Level	9	56290.3	FAAt101
1	FAAt101	k1ttotl1es00	2	Fixed assets	2013	Current Dollars	Level	9	51510.7	FAAt101
2	FAAt101	k1ptotl1es00	3	Private	2013	Current Dollars	Level	9	38567.6	FAAt101
3	FAAt101	k1ntotl1es00	4	Nonresidential	2013	Current Dollars	Level	9	21073.3	FAAt101
4	FAAt101	k1ntotl1eq00	5	Equipment	2013	Current Dollars	Level	9	5897.2	FAAt101

Industry Data¶

GDPbyIndustry (Publication category “GDP-by-Industry”, a default)¶

[28]:

# Info from iTable
desc = "Value Added by Industry"
year="2019"
freq="Q"
industries = "ALL" # from iTable

First, what parameters are needed?

[29]:

display(param_infos["GDPbyIndustry"])

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	MultipleAcceptedFlag	AllValue
0	Frequency	string	A - Annual, Q-Quarterly	1	1	ALL
1	Industry	string	List of industries to retrieve (ALL for All)	1	1	ALL
2	TableID	integer	The unique GDP by Industry table identifier (A...	1	1	ALL
3	Year	integer	List of year(s) of data to retrieve (ALL for All)	1	1	ALL

Get tablename

[30]:

table_param_name, table_param_vals = get_table_param_from_desc("GDPbyIndustry", desc)
display(table_param_vals)
print("That first one looks right.")
table_param_val = table_param_vals.iloc[0,0]

What TableName matches?

	Key	Desc
0	1	Value Added by Industry (A) (Q)
2	6	Components of Value Added by Industry (A)
3	7	Components of Value Added by Industry as a Per...
4	8	Chain-Type Quantity Indexes for Value Added by...
5	9	Percent Changes in Chain-Type Quantity Indexes...
6	10	Real Value Added by Industry (A) (Q)
7	11	Chain-Type Price Indexes for Value Added by In...
8	12	Percent Changes in Chain-Type Price Indexes fo...

Get data

[31]:

tbl = beaapi.get_data(beakey, "GDPbyIndustry", TableID=table_param_val, Year=year, Frequency=freq, Industry=industries)
tbl.head()

[31]:

	TableID	Frequency	Year	Quarter	Industry	IndustrYDescription	DataValue	NoteRef
0	1	Q	2019	I	11	Agriculture, forestry, fishing, and hunting	159.7	1
1	1	Q	2019	I	111CA	Farms	120.9	1
2	1	Q	2019	I	113FF	Forestry, fishing, and related activities	38.8	1
3	1	Q	2019	I	21	Mining	302.1	1
4	1	Q	2019	I	211	Oil and gas extraction	185.6	1

UnderlyingGDPbyIndustry (Publication category “Underlying”, non-default)¶

[35]:

# Info from iTable
desc = "U.Value Added by Industry"
year="2013"
freq="A"

What parameters are needed?

[36]:

display(param_infos["UnderlyingGDPbyIndustry"])
industries = "ALL" # from iTable

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	MultipleAcceptedFlag	AllValue
0	Frequency	string	Q-Quarterly	1	1	ALL
1	Industry	string	List of industries to retrieve (ALL for All)	1	1	ALL
2	TableID	integer	The unique Underlying GDP by Industry table id...	1	1	ALL
3	Year	integer	List of year(s) of data to retrieve (ALL for All)	1	1	ALL

Get tablename

[37]:

table_param_name, table_param_vals = get_table_param_from_desc("UnderlyingGDPbyIndustry", desc)
print("What TableName matches?")
display(table_param_vals)
table_param_val = table_param_vals.iloc[0,0]
print("First one")

What TableName matches?

	Key	Desc
0	210	U.Value Added by Industry (A)

First one

Get data

[38]:

tbl = beaapi.get_data(beakey, "UnderlyingGDPbyIndustry", TableID=table_param_val, Year=year, Frequency=freq, Industry=industries)
tbl.head()

[38]:

	TableID	Frequency	Year	Industry	IndustrYDescription	DataValue	NoteRef
0	210	A	2013	11	Agriculture, forestry, fishing, and hunting	214.3	210
1	210	A	2013	111	Crop production	111.7	210
2	210	A	2013	111CA	Farms	183.3	210
3	210	A	2013	112	Animal production and aquaculture	71.5	210
4	210	A	2013	113FF	Forestry, fishing, and related activities	31.0	210

InputOutput (Publication category “Input-Output”, a default)¶

[43]:

# Info from iTable
desc = "The Use of Commodities by Industries - Sector"
year="2020"

print("What parameters are needed")
display(param_infos["InputOutput"])
#industries = "ALL" # from iTable

What parameters are needed

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	TableID	integer	The unique Input-Output table identifier	1		1
1	Year	integer	List of year(s) of data to retrieve (ALL for All)	1		1	ALL

Get tablename

[44]:

table_param_name, table_param_vals = get_table_param_from_desc("InputOutput", desc)
print("What TableName matches?")
display(table_param_vals)
print("That first one looks good")
table_param_val = table_param_vals.iloc[0,0]

What TableName matches?

	Key	Desc
9	258	The Use of Commodities by Industries - Sector

That first one looks good

Get data

[45]:

tbl = beaapi.get_data(beakey, "InputOutput", TableID=table_param_val, Year=year)
tbl.head()

[45]:

	TableID	Year	RowCode	RowDescr	RowType	ColCode	ColDescr	ColType	DataValue	NoteRef
0	258	2020	11	Agriculture, forestry, fishing, and hunting	Commodity	11	Agriculture, forestry, fishing, and hunting	Industry	106419	258
1	258	2020	11	Agriculture, forestry, fishing, and hunting	Commodity	T001	Total Intermediate	Industry	446013	258
2	258	2020	11	Agriculture, forestry, fishing, and hunting	Commodity	31G	Manufacturing	Industry	309395	258
3	258	2020	11	Agriculture, forestry, fishing, and hunting	Commodity	7	Arts, entertainment, recreation, accommodation...	Industry	8556	258
4	258	2020	11	Agriculture, forestry, fishing, and hunting	Commodity	21	Mining	Industry	168	258

[15]:

# Unsure how to access Publication category "Make Use Framework"
# desc="The Use of Commodities by Industries, After Redefinitions"
# Both methods fail:
#search_result = beaapi.search_metadata(desc, beakey)
#display(search_result)
#table_param_name, table_param_vals = get_table_param_from_desc("UnderlyingGDPbyIndustry", desc) # Tried also with InputOutput, GDPbyIndustry
#display(table_param_vals)

Int’l Transactions, Services, IIP¶

ITA¶

The different tables are associated with different sets of lines (indicators). So some tables aren’t easy to reconstruct exactly.

Note: (in PDF but API error msg confusing) you can’t get all indicators using “AllCountries” (agg’d over countries), even though that would seem easy (and is something like like table 1.1).

Basically you have to look through beaapi.get_parameter_values(beakey, "ITA", "Indicator")

Note also that the following indicators don’t report global sum, so ask for all countries individually: ‘ExpGdsAutoEngAndEngParts’, ‘ExpGdsOthAutoPartsAndAcc’, ‘ExpGdsPassCars’, ‘ExpGdsTrucksBusesSpecPurpVeh’, ‘ImpGdsAutoEngAndEngParts’, ‘ImpGdsOthAutoPartsAndAcc’, ‘ImpGdsPassCars’, ‘ImpGdsTrucksBusesSpecPurpVeh’

[49]:

# Roughly one line from "Table 1.2. U.S. International Transactions, Expanded Detail"
year="2020"
freq="QSA"
cntry="AllCountries"
desc = "Balance on goods"

print("What parameters are needed")
display(param_infos["ITA"])

What parameters are needed

	ParameterName	ParameterDataType	ParameterDescription	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	Indicator	string	The indicator code for the type of transaction...	ALL	1	ALL
1	AreaOrCountry	string	The area or country requested	AllCountries	1	ALL
2	Frequency	string	A - Annual, QSA - Quarterly seasonally adjuste...	ALL	1	ALL
3	Year	string	Year requested	ALL	1	ALL

Get indicator (or get a single country)

[51]:

# areas_ITA = beaapi.get_parameter_values(beakey, 'ITA', 'AreaOrCountry')
ita_indicatorsID = beaapi.get_parameter_values(beakey, 'ITA', 'Indicator')

ind_result = ita_indicatorsID[ita_indicatorsID["Desc"].str.contains(desc)]
display(ind_result)
print("Take the first one")
indicator = "BalGds"

	Key	Desc
2	BalGds	Balance on goods
3	BalGdsServ	Balance on goods and services

Take the first one

Get data

[52]:

tbl = beaapi.get_data(beakey, "ITA", Year=year, AreaOrCountry=cntry, Frequency=freq, Indicator=indicator)
tbl.head()

[52]:

	Indicator	AreaOrCountry	Frequency	Year	TimeSeriesId	TimeSeriesDescription	TimePeriod	CL_UNIT	UNIT_MULT	DataValue	NoteRef
0	BalGds	AllCountries	QSA	2020	TSI_ItaBalGds_QSA	Balance on goods; quarterly seasonally adjusted	2020Q3	USD	6	-245370	Q
1	BalGds	AllCountries	QSA	2020	TSI_ItaBalGds_QSA	Balance on goods; quarterly seasonally adjusted	2020Q4	USD	6	-253125	Q

IntlServTrade¶

Similar issue about different iTables tables being different sets or broken out by country in different ways. Basically have to look at TypeOfService values to line up with tables. Though in this case we can get all types of service of “AllCountries” (agg’d over countries)

[56]:

type_desc = "Sea transport"

print("What parameters are needed")
display(param_infos["IntlServTrade"])

What parameters are needed

	ParameterName	ParameterDataType	ParameterDescription	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	TypeOfService	string	The type of service requested	ALL	1	ALL
1	TradeDirection	string	The trade direction requested	ALL	1	ALL
2	Affiliation	string	The affiliation requested	ALL	1	ALL
3	AreaOrCountry	string	The area or country requested	AllCountries	1	ALL
4	Year	string	The year requested	ALL	1	ALL

Get type

[58]:

# cntr_IST = beaapi.get_parameter_values(beakey, 'IntlServTrade', 'AreaOrCountry')
ist_Type = beaapi.get_parameter_values(beakey, 'IntlServTrade', 'TypeOfService')
type_result = ist_Type[ist_Type["Desc"].str.contains(type_desc)]
display(type_result)
type_of_service = type_result.iloc[0,0]

	Key	Desc
104	TransportSea	Sea transport services

Get data

[59]:

tbl = beaapi.get_data(beakey, "IntlServTrade", AreaOrCountry="AllCountries",TypeOfService=type_of_service, Year="1999")
tbl.head()

[59]:

	TypeOfService	TradeDirection	Affiliation	AreaOrCountry	Year	TimeSeriesId	TimeSeriesDescription	TimePeriod	CL_UNIT	UNIT_MULT	DataValue	NoteRef
0	TransportSea	Exports	Affiliated	AllCountries	1999	TSI_IstTransportSeaExpAllCountriesAff_A	Exports of sea transport services; all countri...	1999	USD	6	NaN	.....
1	TransportSea	Exports	AllAffiliations	AllCountries	1999	TSI_IstTransportSeaExpAllCountriesAllAff_A	Exports of sea transport services; all countries	1999	USD	6	10489.0
2	TransportSea	Exports	Unaffiliated	AllCountries	1999	TSI_IstTransportSeaExpAllCountriesUnaff_A	Exports of sea transport services; all countri...	1999	USD	6	10489.0
3	TransportSea	Exports	UsAffiliates	AllCountries	1999	TSI_IstTransportSeaExpAllCountriesUsAff_A	Exports of sea transport services; all countri...	1999	USD	6	NaN	.....
4	TransportSea	Exports	UsParents	AllCountries	1999	TSI_IstTransportSeaExpAllCountriesUsPar_A	Exports of sea transport services; all countri...	1999	USD	6	NaN	.....

IIP¶

Similar issues as above, but now TypeOfInvestment are the lines in tables. Does allow all TypeOfInvestment for “AllCountries” (agg’d over countries)

[61]:

type_desc = "Net international investment position excluding financial derivatives"

print("What parameters are needed")
display(param_infos["IIP"])

What parameters are needed

	ParameterName	ParameterDataType	ParameterDescription	ParameterDefaultValue	MultipleAcceptedFlag	AllValue
0	TypeOfInvestment	string	Type of investment	ALL	1	ALL
1	Component	string	Component of changes in position	ALL	1	ALL
2	Frequency	string	A - Annual, QNSA - Quarterly not seasonally ad...	ALL	1	ALL
3	Year	string	Year requested	ALL	1	ALL

Get Type of Investment

[62]:

# year_IIP = beaapi.get_parameter_values(beakey, 'IIP', 'Year') #if we wanted to get list of years
iip_Type = beaapi.get_parameter_values(beakey, 'IIP', 'TypeOfInvestment')
type_result = iip_Type[iip_Type["Desc"].str.contains(type_desc.lower())] #Note descriptions don't exaclty match lines so make lower case
display(type_result)
type_of_inv = type_result.iloc[0,0]

	Key	Desc
343	NetExclFinDeriv	U.S. net international investment position exc...

Get data

[63]:

tbl = beaapi.get_data(beakey, "IIP", AreaOrCountry="AllCountries",TypeOfInvestment=type_of_inv,Year="2020",Frequency="QNSA")
tbl.head()

[63]:

	TypeOfInvestment	Component	Frequency	Year	TimeSeriesId	TimeSeriesDescription	TimePeriod	CL_UNIT	UNIT_MULT	DataValue
0	NetExclFinDeriv	Pos	QNSA	2020	TSI_IipNetExclFinDerivPos_QNSA	U.S. net international investment position exc...	2020Q1	USD	6	-12150967
1	NetExclFinDeriv	Pos	QNSA	2020	TSI_IipNetExclFinDerivPos_QNSA	U.S. net international investment position exc...	2020Q2	USD	6	-13018024
2	NetExclFinDeriv	Pos	QNSA	2020	TSI_IipNetExclFinDerivPos_QNSA	U.S. net international investment position exc...	2020Q3	USD	6	-13794338
3	NetExclFinDeriv	Pos	QNSA	2020	TSI_IipNetExclFinDerivPos_QNSA	U.S. net international investment position exc...	2020Q4	USD	6	-14004615

Direct Investment & MNEs¶

See the separate guide iTables_MNE

Regional¶

[64]:

# Regional actually has the table name at the end of the description, so no need for get_table_param_from_desc()!
table_param_val= "SQGDP1"
year="2021"

print("What parameters are needed")
display(param_infos["Regional"])

What parameters are needed

	ParameterName	ParameterDataType	ParameterDescription	ParameterIsRequiredFlag	MultipleAcceptedFlag	ParameterDefaultValue
0	GeoFips	string	Comma-delimited list of 5-character geographic...	1	1	<NA>
1	LineCode	integer	Line code for a statistic or industry	1	0	<NA>
2	TableName	string	Regional income or product table to retrieve	1	0
3	Year	string	Comma-delimted list of years; LAST5 for latest...	0	1	LAST5

[ ]:

linecode="ALL" # Mentioned in PDF
# Otherwise, could do
# pos_line_codes = beaapi.get_parameter_values_filtered(beakey, "Regional", "LineCode", TableName=table_param_val)
# display(pos_line_codes)

geo_fips = "00000" # Note: Not mentioned in docs, but common. See pdf for other common values
# Otherwise, could do
# pos_geo_codes = beaapi.get_parameter_values_filtered(beakey, "Regional", "GeoFips", TableName=table_param_val, LineCode=linecode)
# display(pos_geo_codes)

Get data

[66]:

tbl = beaapi.get_data(beakey, "Regional", TableName=table_param_val, Year=year, GeoFips=geo_fips, LineCode=linecode)
tbl.head()

[66]:

	Code	GeoName	TimePeriod	Description	CL_UNIT	UNIT_MULT	DataValue
0	SQGDP1-3	United States	2021Q1	Current-dollar GDP (millions of current dollars)	Millions of current dollars	6	22038226.0
1	SQGDP1-3	United States	2021Q2	Current-dollar GDP (millions of current dollars)	Millions of current dollars	6	22740959.0
2	SQGDP1-3	United States	2021Q3	Current-dollar GDP (millions of current dollars)	Millions of current dollars	6	23202344.0
3	SQGDP1-3	United States	2021Q4	Current-dollar GDP (millions of current dollars)	Millions of current dollars	6	24002815.0
4	SQGDP1-1	United States	2021Q1	Real GDP (millions of chained 2012 dollars)	Millions of chained 2012 dollars	6	19055655.0

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How to translate iTable tables to API queries¶

General info¶

Other resources¶

Setup¶

Code utilities¶

How to build query from iTables¶

National Data - GDP & Personal Income¶

NIPA (publication category “GDP and Personal Income”, default)¶

NIUnderlyingDetail (Publication Category “Underlying Details”, non-default)¶

National Data - Fixed Assets¶

FixedAssets¶

Industry Data¶

GDPbyIndustry (Publication category “GDP-by-Industry”, a default)¶

UnderlyingGDPbyIndustry (Publication category “Underlying”, non-default)¶

InputOutput (Publication category “Input-Output”, a default)¶

Int’l Transactions, Services, IIP¶

ITA¶

IntlServTrade¶

IIP¶

Direct Investment & MNEs¶

Regional¶

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