FinzData/macro-income-spending

US macro data: Income and Spending (166 series)

166 income and spending series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-11-01 on) of each series' latest values. It is refreshed weekly.

import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-income-spending/data/long.parquet")      # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-income-spending/data/wide_weekly.parquet")  # one column per series

or datasets.load_dataset("FinzData/macro-income-spending"). Wide tables by frequency: wide_weekly (6 series), wide_monthly (109 series), wide_quarterly (51 series). series.csv lists every series with its title, units, frequency and how far back the full history goes.

The full history, point in time, through one API

The FinzData API serves every series back to its first observation, every revision (what was known on any past date, so backtests do not peek at later revisions), and daily updates, in the same format:

import finzdata as fz                     # pip install finzdata
c = fz.Client()                           # free API key: https://api.finzdata.com/
c.macro("PSAVERT")                        # full history, latest values
c.macro("PSAVERT", vintage="first")       # as first released
c.macro("PSAVERT", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="income_spending")          # the catalogue for this category

Most-watched series in this package

ID	Title	Units	Frequency	Full history from
PSAVERT	Personal Saving Rate	Percent	Monthly	1959
PCE	Personal Consumption Expenditures	Billions of Dollars	Monthly	1959
TOTALSA	Total Vehicle Sales	Millions of Units	Monthly	1976
GASDESW	US Diesel Sales Price	Dollars per Gallon	Weekly, Ending Monday	1994
PCEC96	Real Personal Consumption Expenditures	Billions of Chained 2017 Dollars	Monthly	1959
DSPIC96	Real Disposable Personal Income	Billions of Chained 2017 Dollars	Monthly	1959
TDSP	Household Debt Service Payments as a Percent of Disposable Personal Income	Percent	Quarterly	1980
RSXFS	Advance Retail Sales: Retail Trade	Millions of Dollars	Monthly	1992
HTRUCKSSAAR	Motor Vehicle Retail Sales: Heavy Weight Trucks	Millions of Units	Monthly	1967
RSAFS	Advance Retail Sales: Retail Trade and Food Services	Millions of Dollars	Monthly	1992
ALTSALES	Light Weight Vehicle Sales: Autos and Light Trucks	Millions of Units	Monthly	1976
DSPI	Disposable Personal Income	Billions of Dollars	Monthly	1959
WRMFNS	Retail Money Market Funds	Billions of Dollars	Weekly, Ending Monday	1980
A229RX0	Real Disposable Personal Income: Per Capita	Chained 2017 Dollars	Monthly	1959
MSPNHSUS	Median Sales Price for New Houses Sold in the United States	Dollars	Monthly	1963
RRSFS	Advance Real Retail and Food Services Sales	Millions of 1982-84 CPI Adjusted Dollars	Monthly	1992
ECOMPCTSA	E-Commerce Retail Sales as a Percent of Total Sales	Percent	Quarterly	1999
PI	Personal Income	Billions of Dollars	Monthly	1946
ECOMSA	E-Commerce Retail Sales	Millions of Dollars	Quarterly	1999
MRTSSM44000USS	Retail Sales: Retail Trade	Millions of Dollars	Monthly	1992
RETAILIRSA	Retailers: Inventories to Sales Ratio	Ratio	Monthly, End of Period	1992
CDSP	Consumer Debt Service Payments as a Percent of Disposable Personal Income	Percent	Quarterly	1980
PMSAVE	Personal Saving	Billions of Dollars	Monthly	1959
RPI	Real Personal Income	Billions of Chained 2017 Dollars	Monthly	1959
SNDR	National Rate: Savings	Percent	Monthly	2021

All 166 series: series.csv. Browse them at https://finzdata.com/macro/category/income-spending.

More from FinzData

• Every macro category: https://huggingface.co/FinzData · index on GitHub: https://github.com/FinzData/macro-data
• Survivorship-free US stock prices since 2000, COT positioning, 13F holdings, insider trades, short interest
• Pricing (history bundles from $195 once): https://finzdata.com/pricing
• Python client (open source): https://github.com/FinzData/finzdata-python

Source: U.S. government agencies via FRED/ALFRED (Federal Reserve Bank of St. Louis). Compiled by FinzData. Data for research, not investment advice.