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series_id
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363 values
date
date32
value
float64
-26,116,371
196M
A006RL1Q225SBEA
2026-01-01
7.2
A006RL1Q225SBEA
2026-04-01
4.6
A006RL1Q225SBEA
2026-07-01
19.4
A007RL1Q225SBEA
2026-01-01
6.1
A007RL1Q225SBEA
2026-04-01
7.7
A007RL1Q225SBEA
2026-07-01
9.4
A008RL1Q225SBEA
2026-01-01
9.9
A008RL1Q225SBEA
2026-04-01
9
A008RL1Q225SBEA
2026-07-01
12.7
A027RC1Q027SBEA
2026-01-01
26,625.746
A027RC1Q027SBEA
2026-04-01
27,200.595
A068RC1
2025-11-01
22,163.3
A068RC1
2025-12-01
22,222.1
A068RC1
2026-01-01
22,274.2
A068RC1
2026-02-01
22,397.6
A068RC1
2026-03-01
22,593.2
A068RC1
2026-04-01
22,764.2
A068RC1
2026-05-01
22,943.2
A068RC1
2026-06-01
23,020.1
A068RC1
2026-07-01
23,071.5
A068RC1
2026-08-01
23,264.9
A068RC1
2026-09-01
23,377.9
A446RC1Q027SBEA
2026-01-01
4,154.402
A446RC1Q027SBEA
2026-04-01
4,565.549
A451RC1Q027SBEA
2026-01-01
19,215.588
A451RC1Q027SBEA
2026-04-01
19,749.163
A455RC1Q027SBEA
2026-01-01
16,407.81
A455RC1Q027SBEA
2026-04-01
16,860.501
A464RC1Q027SBEA
2026-01-01
3,163.233
A464RC1Q027SBEA
2026-04-01
3,502.472
A679RC1Q027SBEA
2026-01-01
1,586.328
A679RC1Q027SBEA
2026-04-01
1,645.684
A679RC1Q027SBEA
2026-07-01
1,765.37
A679RL1Q225SBEA
2026-01-01
32.8
A679RL1Q225SBEA
2026-04-01
10.7
A679RL1Q225SBEA
2026-07-01
23.2
A679RX1Q020SBEA
2026-01-01
1,713.948
A679RX1Q020SBEA
2026-04-01
1,758.194
A679RX1Q020SBEA
2026-07-01
1,852.47
A680RC1Q027SBEA
2026-01-01
360.299
A680RC1Q027SBEA
2026-04-01
381.174
A680RC1Q027SBEA
2026-07-01
406.147
A782RC1Q027SBEA
2026-01-01
1,130.098
A782RC1Q027SBEA
2026-04-01
1,144.428
A782RC1Q027SBEA
2026-07-01
1,173.7
ABCOMP
2025-11-05
401.023319
ABCOMP
2025-11-12
401.915703
ABCOMP
2025-11-19
393.730458
ABCOMP
2025-11-26
394.470731
ABCOMP
2025-12-03
395.345697
ABCOMP
2025-12-10
402.822327
ABCOMP
2025-12-17
407.41145
ABCOMP
2025-12-24
415.036351
ABCOMP
2025-12-31
421.141259
ABCOMP
2026-01-07
429.78781
ABCOMP
2026-01-14
439.294988
ABCOMP
2026-01-21
444.658345
ABCOMP
2026-01-28
451.978876
ABCOMP
2026-02-04
453.777693
ABCOMP
2026-02-11
454.458404
ABCOMP
2026-02-18
450.116532
ABCOMP
2026-02-25
456.943558
ABCOMP
2026-03-04
436.866666
ABCOMP
2026-03-11
432.167823
ABCOMP
2026-03-18
418.629987
ABCOMP
2026-03-25
424.455212
ABCOMP
2026-04-01
437.958731
ABCOMP
2026-04-08
432.555386
ABCOMP
2026-04-15
439.33525
ABCOMP
2026-04-22
449.515934
ABCOMP
2026-04-29
455.897957
ABCOMP
2026-05-06
451.417776
ABCOMP
2026-05-13
458.343065
ABCOMP
2026-05-20
459.998063
ABCOMP
2026-05-27
460.180164
ABCOMP
2026-06-03
465.724955
ABCOMP
2026-06-10
469.574932
ABCOMP
2026-06-17
473.975372
ABCOMP
2026-06-24
475.841562
ABCOMP
2026-07-01
483.763818
ABCOMP
2026-07-08
480.091527
ABCOMP
2026-07-15
480.889895
ABCOMP
2026-07-22
482.101433
ABCOMP
2026-07-29
495.239084
ABCOMP
2026-08-05
489.704625
ABCOMP
2026-08-12
494.262781
ABCOMP
2026-08-19
488.44825
ABCOMP
2026-08-26
494.455125
ABCOMP
2026-09-02
496.924031
ABCOMP
2026-09-09
498.11075
ABCOMP
2026-09-16
498.012625
ABCOMP
2026-09-23
501.671969
ABCOMP
2026-09-30
504.395406
ABCOMP
2026-10-07
505.923188
ABCOMP
2026-10-14
510.73225
ABCOMP
2026-10-21
504.381406
ABCOMP
2026-10-28
506.686719
AIRRPMTSID11
2025-11-01
94,240,327
AIRRPMTSID11
2025-12-01
96,151,050
AIRRPMTSID11
2026-01-01
95,289,748
End of preview. Expand in Data Studio

US macro data: Other Indicators (363 series)

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

or datasets.load_dataset("FinzData/macro-other-indicators"). Wide tables by frequency: wide_daily (36 series), wide_weekly (31 series), wide_monthly (151 series), wide_quarterly (132 series), wide_annual (11 series), wide_other (2 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("APU0000703112")                        # full history, latest values
c.macro("APU0000703112", vintage="first")       # as first released
c.macro("APU0000703112", as_of="2020-03-01")    # exactly what was known on that date
c.macro_series(category="other_indicators")          # the catalogue for this category

Most-watched series in this package

ID Title Units Frequency Full history from
APU0000703112 Average Price: Ground Beef, 100% Beef (Cost per Pound/453.6 Grams) in U.S. City Average U.S. Dollars Monthly 1984
APU0000708111 Average Price: Eggs, Grade A, Large (Cost per Dozen) in U.S. City Average U.S. Dollars Monthly 1980
GASREGW US Regular All Formulations Gas Price Dollars per Gallon Weekly, Ending Monday 1990
CP Corporate Profits After Tax (without IVA and CCAdj) Billions of Dollars Quarterly 1946
DPRIME Bank Prime Loan Rate Percent Daily 1955
FYFSD Federal Surplus or Deficit [-] Millions of Dollars Annual, Fiscal Year 1901
MMMFFAQ027S Money Market Funds; Total Financial Assets, Level Millions of U.S. Dollars Quarterly, End of Period 1945
APU000072610 Average Price: Electricity per Kilowatt-Hour in U.S. City Average U.S. Dollars Monthly 1978
APU0000709112 Average Price: Milk, Fresh, Whole, Fortified (Cost per Gallon/3.8 Liters) in U.S. City Average U.S. Dollars Monthly 1995
FEDTARMD FOMC Summary of Economic Projections for the Fed Funds Rate, Median Percent Annual 2015
GDPNOW GDPNow Percent Change at Annual Rate Quarterly 2011
PRIME Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates Percent Not Applicable 1955
WFRBST01134 Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) Percent of Aggregate Quarterly 1989
MTSDS133FMS Federal Surplus or Deficit [-] Millions of Dollars Monthly 1980
IUDSOIA Daily Sterling Overnight Index Average (SONIA) Rate Percent Daily 1997
APU0000FF1101 Average Price: Chicken Breast, Boneless (Cost per Pound/453.6 Grams) in U.S. City Average U.S. Dollars Monthly 2006
BOGZ1FL192090005Q Households; Net Worth, Level Millions of U.S. Dollars Quarterly, End of Period 1987
OBFR Overnight Bank Funding Rate Percent Daily 2016
RPONTTLD Overnight Repurchase Agreements: Total Securities Purchased by the Federal Reserve in the Temporary Open Market Operations Billions of US Dollars Daily 2000
WJFUELUSGULF Kerosene-Type Jet Fuel Prices: U.S. Gulf Coast Dollars per Gallon Weekly, Ending Friday 1990
WFRBLTP1246 Net Worth Held by the Top 0.1% (99.9th to 100th Wealth Percentiles) Millions of Dollars Quarterly 1989
APU0000702111 Average Price: Bread, White, Pan (Cost per Pound/453.6 Grams) in U.S. City Average U.S. Dollars Monthly 1980
GPDIC1 Real Gross Private Domestic Investment Billions of Chained 2017 Dollars Quarterly 1947
SWPT Assets: Central Bank Liquidity Swaps: Central Bank Liquidity Swaps: Wednesday Level Millions of U.S. Dollars Weekly, As of Wednesday 2002
BOGZ1FL153064486Q Households and Nonprofit Organizations; Directly and Indirectly Held Corporate Equities as a Percentage of Financial Assets; Assets, Level Percent Quarterly, End of Period 1945

All 363 series: series.csv. Browse them at https://finzdata.com/macro/category/other-indicators.

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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