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series_id
large_stringclasses
60 values
date
date32
value
float64
-1.1
3.06k
CUURX000SEHF01
2025-11-01
223.545
CUURX000SEHF01
2025-12-01
224.869
CUURX000SEHF01
2026-01-01
229.218
CUURX000SEHF01
2026-02-01
227.517
CUURX000SEHF01
2026-03-01
228.25
CUURX000SEHF01
2026-04-01
230.259
CUURX000SEHF01
2026-05-01
233.214
CUURX000SEHF01
2026-06-01
235.795
CUURX000SEHF01
2026-07-01
234.226
CUURX000SEHF01
2026-08-01
234.632
CUUS0100SEHF01
2026-01-01
322.234
CUUS0300SEHF01
2026-01-01
258.604
CUUSA000SEHF01
2026-01-01
313.427
CUUSX000SEHF01
2026-01-01
230.709
IPN221112NQ
2026-01-01
95.3719
IPN221112NQ
2026-04-01
90.4891
IPN221112SQ
2026-01-01
101.705
IPN221112SQ
2026-04-01
99.343
IPN221113NQ
2026-01-01
99.669
IPN221113NQ
2026-04-01
95.2565
IPN221114T8SQ
2026-01-01
224.9678
IPN221114T8SQ
2026-04-01
251.219
IPN22112CNQ
2026-01-01
118.6133
IPN22112CNQ
2026-04-01
123.0843
IPN22112CSQ
2026-01-01
125.8335
IPN22112CSQ
2026-04-01
126.8041
IPN22112MSQ
2026-01-01
106.2478
IPN22112MSQ
2026-04-01
108.4003
IPN22112RSQ
2026-01-01
111.1473
IPN22112RSQ
2026-04-01
110.0979
PCU221122221122413
2025-11-01
235.99
PCU221122221122413
2025-12-01
232.314
PCU221122221122413
2026-01-01
234.115
PCU221122221122413
2026-02-01
232.966
PCU221122221122413
2026-03-01
239.745
PCU221122221122413
2026-04-01
238.149
PCU221122221122413
2026-05-01
244.597
PCU221122221122413
2026-06-01
273.411
PCU221122221122413
2026-07-01
271.236
PCU221122221122413
2026-08-01
275.054
PCU221122221122415
2025-11-01
229.545
PCU221122221122415
2025-12-01
229.812
PCU221122221122415
2026-01-01
236.689
PCU221122221122415
2026-02-01
230.576
PCU221122221122415
2026-03-01
232.693
PCU221122221122415
2026-04-01
232.529
PCU221122221122415
2026-05-01
233.397
PCU221122221122415
2026-06-01
241.515
PCU221122221122415
2026-07-01
242.283
PCU221122221122415
2026-08-01
243.048
PRS31006022
2026-01-01
0.1
PRS31006022
2026-04-01
4
PRS31006151
2026-01-01
3.6
PRS31006151
2026-04-01
1.7
PRS32006022
2026-01-01
1.2
PRS32006022
2026-04-01
0.9
PRS32006041
2026-01-01
-0.8
PRS32006041
2026-04-01
-0.6
PRS88003022
2026-01-01
1
PRS88003022
2026-04-01
0.6
REV2211TPSA
2026-01-01
3
REV2211TPSA
2026-04-01
-1.1
SMU01000004322110001
2025-11-01
10.4
SMU01000004322110001
2025-12-01
10.5
SMU01000004322110001SA
2025-11-01
10.471276
SMU01000004322110001SA
2025-12-01
10.463572
SMU06000004322110001
2025-11-01
22.1
SMU06000004322110001
2025-12-01
22
SMU06000004322110001SA
2025-11-01
21.886808
SMU06000004322110001SA
2025-12-01
21.900099
SMU20000004322110001
2025-11-01
4.5
SMU20000004322110001
2025-12-01
4.5
SMU20000004322110001
2026-01-01
4.5
SMU20000004322110001
2026-02-01
4.5
SMU20000004322110001
2026-03-01
4.5
SMU20000004322110001
2026-04-01
4.5
SMU20000004322110001
2026-05-01
4.5
SMU20000004322110001
2026-06-01
4.5
SMU20000004322110001
2026-07-01
4.6
SMU20000004322110001
2026-08-01
4.5
SMU20000004322110001SA
2025-11-01
4.5
SMU20000004322110001SA
2025-12-01
4.5
SMU20000004322110001SA
2026-01-01
4.5
SMU20000004322110001SA
2026-02-01
4.5
SMU20000004322110001SA
2026-03-01
4.5
SMU20000004322110001SA
2026-04-01
4.5
SMU20000004322110001SA
2026-05-01
4.5
SMU20000004322110001SA
2026-06-01
4.5
SMU20000004322110001SA
2026-07-01
4.6
SMU20000004322110001SA
2026-08-01
4.5
SMU29000004322110001
2025-11-01
8.1
SMU29000004322110001
2025-12-01
8.2
SMU29000004322110001
2026-01-01
8.1
SMU29000004322110001
2026-02-01
8.1
SMU29000004322110001
2026-03-01
8.1
SMU29000004322110001
2026-04-01
8.2
SMU29000004322110001
2026-05-01
8.2
SMU29000004322110001
2026-06-01
8.3
SMU29000004322110001
2026-07-01
8.3
SMU29000004322110001
2026-08-01
8.3
End of preview. Expand in Data Studio

US macro data: Other (60 series)

60 other 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-other/data/long.parquet")      # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-other/data/wide_monthly.parquet")  # one column per series

or datasets.load_dataset("FinzData/macro-other"). Wide tables by frequency: wide_monthly (42 series), wide_quarterly (14 series), wide_semiannual (4 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("CUURX000SEHF01")                        # full history, latest values
c.macro("CUURX000SEHF01", vintage="first")       # as first released
c.macro("CUURX000SEHF01", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="other")          # the catalogue for this category

Most-watched series in this package

ID Title Units Frequency Full history from
CUURX000SEHF01 Consumer Price Index for All Urban Consumers: Electricity in Size Class B/C Index Dec 1997=100 Monthly 1997
CUUS0100SEHF01 Consumer Price Index for All Urban Consumers: Electricity in Northeast Index 1982-1984=100 Semiannual 1984
CUUS0300SEHF01 Consumer Price Index for All Urban Consumers: Electricity in South Index 1982-1984=100 Semiannual 1984
CUUSA000SEHF01 Consumer Price Index for All Urban Consumers: Electricity in Size Class A Index Dec 1986=100 Semiannual 1987
CUUSX000SEHF01 Consumer Price Index for All Urban Consumers: Electricity in Size Class B/C Index Dec 1997=100 Semiannual 1998
IPN221112NQ Industrial Production: Utilities: Fossil Fuel Electric Power Generation (NAICS = 221112) Index 2017=100 Quarterly 2001
IPN221112SQ Industrial Production: Utilities: Fossil Fuel Electric Power Generation (NAICS = 221112) Index 2017=100 Quarterly 2001
IPN221113NQ Industrial Production: Utilities: Nuclear Electric Power Generation (NAICS = 221113) Index 2017=100 Quarterly 1972
IPN221114T8SQ Industrial Production: Utilities: Renewables and Other Electric Power Generation (NAICS = 221114-8) Index 2017=100 Quarterly 2001
IPN22112CNQ Industrial Production: Utilities: Commercial and Other Electricity Sales (NAICS = 22112pt.) Index 2017=100 Quarterly 1972
IPN22112CSQ Industrial Production: Utilities: Commercial and Other Electricity Sales (NAICS = 22112pt.) Index 2017=100 Quarterly 1972
IPN22112MSQ Industrial Production: Utilities: Industrial Electricity Sales (NAICS = 22112pt.) Index 2017=100 Quarterly 1972
IPN22112RSQ Industrial Production: Utilities: Residential Electricity Sales (NAICS = 22112pt.) Index 2017=100 Quarterly 1972
PCU221122221122413 Producer Price Index by Industry: Electric Power Distribution: Residential Electric Power for East North Central Census Division Index Dec 1990=100 Monthly 1990
PCU221122221122415 Producer Price Index by Industry: Electric Power Distribution: Residential Electric Power for South Atlantic Census Division Index Dec 1990=100 Monthly 1990
PRS31006022 Manufacturing, Durable Goods Sector: Average Weekly Hours for All Workers Percent Change at Annual Rate Quarterly 1987
PRS31006151 Manufacturing, Durable Goods Sector: Real Hourly Compensation for All Workers Percent Change from Quarter One Year Ago Quarterly 1988
PRS32006022 Manufacturing, Nondurable Goods Sector: Average Weekly Hours for All Workers Percent Change at Annual Rate Quarterly 1987
PRS32006041 Manufacturing, Nondurable Goods Sector: Real Sectoral Output for All Workers Percent Change from Quarter One Year Ago Quarterly 1988
PRS88003022 Nonfinancial Corporations Sector: Average Weekly Hours for Employees Percent Change at Annual Rate Quarterly 1947
REV2211TPSA Total Revenue for 2211: Electric Power Generation, Transmission and Distribution, Establishments Subject to Federal Income Tax Percent Change Quarterly 2010
SMU01000004322110001 All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in Alabama Thousands of Persons Monthly 1990
SMU01000004322110001SA All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in Alabama Thousands of Persons Monthly 1990
SMU06000004322110001 All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in California Thousands of Persons Monthly 1990
SMU06000004322110001SA All Employees: Transportation and Utilities: Electric Power Generation, Transmission and Distribution in California Thousands of Persons Monthly 1990

All 60 series: series.csv. Browse them at https://finzdata.com/macro/category/other.

More from FinzData

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

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