---
license: other
license_name: public-domain-source-us-government
pretty_name: "US Macro Data: Other Indicators"
language:
- en
tags:
- finance
- economics
- macroeconomics
- time-series
- fred
- other-indicators
- quant
- backtesting
size_categories:
- 10K<n<100K
configs:
- config_name: long
  data_files: data/long.parquet
  default: true
- config_name: wide_daily
  data_files: data/wide_daily.parquet
- config_name: wide_weekly
  data_files: data/wide_weekly.parquet
- config_name: wide_monthly
  data_files: data/wide_monthly.parquet
- config_name: wide_quarterly
  data_files: data/wide_quarterly.parquet
- config_name: wide_annual
  data_files: data/wide_annual.parquet
- config_name: wide_other
  data_files: data/wide_other.parquet
---

# 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** (2025-11-02 on) of each series' latest values. It is refreshed weekly.

```python
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:

```python
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
- 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.