series_id large_stringclasses 59
values | date date32 | value float64 -3.65 5,565B |
|---|---|---|
APU0000702212 | 2025-12-01 | 2.677 |
APU0000702212 | 2026-01-01 | 2.729 |
APU0000702212 | 2026-02-01 | 2.687 |
APU0000702212 | 2026-03-01 | 2.747 |
APU0000702212 | 2026-04-01 | 2.735 |
APU0000702212 | 2026-05-01 | 2.814 |
APU000072511 | 2025-12-01 | 3.66 |
APU000072511 | 2026-01-01 | 3.579 |
APU000072511 | 2026-02-01 | 3.99 |
APU000072511 | 2026-03-01 | 5.045 |
APU000072511 | 2026-04-01 | 5.291 |
APU000072511 | 2026-05-01 | 5.293 |
APU000072511 | 2026-06-01 | 4.713 |
APU000072511 | 2026-07-01 | 4.799 |
APU000072511 | 2026-08-01 | 5.145 |
APU000072511 | 2026-09-01 | 5.922 |
APU000074714 | 2025-12-01 | 3.05 |
APU000074714 | 2026-01-01 | 2.961 |
APU000074714 | 2026-02-01 | 3.065 |
APU000074714 | 2026-03-01 | 3.843 |
APU000074714 | 2026-04-01 | 4.263 |
APU000074714 | 2026-05-01 | 4.651 |
APU000074714 | 2026-06-01 | 4.195 |
APU000074714 | 2026-07-01 | 4.094 |
APU000074714 | 2026-08-01 | 4.2 |
APU000074714 | 2026-09-01 | 4.482 |
APU000074716 | 2025-12-01 | 3.958 |
APU000074716 | 2026-01-01 | 3.861 |
APU000074716 | 2026-02-01 | 3.983 |
APU000074716 | 2026-03-01 | 4.757 |
APU000074716 | 2026-04-01 | 5.19 |
APU000074716 | 2026-05-01 | 5.55 |
APU000074716 | 2026-06-01 | 5.15 |
APU000074716 | 2026-07-01 | 5.027 |
APU000074716 | 2026-08-01 | 5.134 |
APU000074716 | 2026-09-01 | 5.426 |
APU00007471A | 2025-12-01 | 3.195 |
APU00007471A | 2026-01-01 | 3.104 |
APU00007471A | 2026-02-01 | 3.211 |
APU00007471A | 2026-03-01 | 3.988 |
APU00007471A | 2026-04-01 | 4.409 |
APU00007471A | 2026-05-01 | 4.793 |
APU00007471A | 2026-06-01 | 4.346 |
APU00007471A | 2026-07-01 | 4.242 |
APU00007471A | 2026-08-01 | 4.348 |
APU00007471A | 2026-09-01 | 4.631 |
APUS12A74714 | 2025-12-01 | 3.02 |
APUS12A74714 | 2026-01-01 | 2.93 |
APUS12A74714 | 2026-02-01 | 2.955 |
APUS12A74714 | 2026-03-01 | 3.617 |
APUS12A74714 | 2026-04-01 | 4.112 |
APUS12A74714 | 2026-05-01 | 4.554 |
APUS12A74714 | 2026-06-01 | 4.204 |
APUS12A74714 | 2026-07-01 | 4.074 |
APUS12A74714 | 2026-08-01 | 4.125 |
APUS12A74714 | 2026-09-01 | 4.428 |
APUS12A7471A | 2025-12-01 | 3.176 |
APUS12A7471A | 2026-01-01 | 3.082 |
APUS12A7471A | 2026-02-01 | 3.107 |
APUS12A7471A | 2026-03-01 | 3.76 |
APUS12A7471A | 2026-04-01 | 4.249 |
APUS12A7471A | 2026-05-01 | 4.692 |
APUS12A7471A | 2026-06-01 | 4.352 |
APUS12A7471A | 2026-07-01 | 4.223 |
APUS12A7471A | 2026-08-01 | 4.275 |
APUS12A7471A | 2026-09-01 | 4.574 |
APUS12B7471A | 2025-12-01 | 3.144 |
APUS12B7471A | 2026-01-01 | 3.039 |
APUS12B7471A | 2026-02-01 | 3.108 |
APUS12B7471A | 2026-03-01 | 3.799 |
APUS12B7471A | 2026-04-01 | 4.241 |
APUS12B7471A | 2026-05-01 | 4.655 |
APUS12B7471A | 2026-06-01 | 4.25 |
APUS12B7471A | 2026-07-01 | 4.195 |
APUS12B7471A | 2026-08-01 | 4.229 |
APUS12B7471A | 2026-09-01 | 4.563 |
APUS23A74714 | 2025-12-01 | 3.113 |
APUS23A74714 | 2026-01-01 | 2.983 |
APUS23A74714 | 2026-02-01 | 3.075 |
APUS23A74714 | 2026-03-01 | 3.91 |
APUS23A74714 | 2026-04-01 | 4.444 |
APUS23A74714 | 2026-05-01 | 5.013 |
APUS23A74714 | 2026-06-01 | 4.337 |
APUS23A74714 | 2026-07-01 | 4.164 |
APUS23A74714 | 2026-08-01 | 4.347 |
APUS23A74714 | 2026-09-01 | 4.625 |
APUS23B74714 | 2025-12-01 | 2.844 |
APUS23B74714 | 2026-01-01 | 2.8 |
APUS23B74714 | 2026-02-01 | 2.887 |
APUS23B74714 | 2026-03-01 | 3.595 |
APUS23B74714 | 2026-04-01 | 3.922 |
APUS23B74714 | 2026-05-01 | 4.674 |
APUS23B74714 | 2026-06-01 | 4.168 |
APUS23B74714 | 2026-07-01 | 4.066 |
APUS23B74714 | 2026-08-01 | 4.192 |
APUS23B74714 | 2026-09-01 | 4.458 |
APUS23B74716 | 2025-12-01 | 4.1 |
APUS23B74716 | 2026-01-01 | 4.063 |
APUS23B74716 | 2026-02-01 | 4.131 |
APUS23B74716 | 2026-03-01 | 4.8 |
US macro data: Commodities (59 series)
59 commodities 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.
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-commodities/data/long.parquet") # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-commodities/data/wide_daily.parquet") # one column per series
or datasets.load_dataset("FinzData/macro-commodities"). Wide tables by frequency: wide_daily (7 series), wide_weekly (4 series), wide_monthly (37 series), wide_quarterly (11 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("MSPUS") # full history, latest values
c.macro("MSPUS", vintage="first") # as first released
c.macro("MSPUS", as_of="2020-03-01") # exactly what was known on that date
c.macro_series(category="commodities") # the catalogue for this category
Most-watched series in this package
| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
MSPUS |
Median Sales Price of New Houses Sold for the United States | Dollars | Quarterly | 1963 |
DCOILWTICO |
Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma | Dollars per Barrel | Daily | 1986 |
ASPUS |
Average Sales Price of Houses Sold for the United States | Dollars | Quarterly | 1963 |
APU000074714 |
Average Price: Gasoline, Unleaded Regular (Cost per Gallon/3.785 Liters) in U.S. City Average | U.S. Dollars | Monthly | 1976 |
DHHNGSP |
Henry Hub Natural Gas Spot Price | Dollars per Million BTU | Daily | 1997 |
WTISPLC |
Spot Crude Oil Price: West Texas Intermediate (WTI) | Dollars per Barrel | Monthly | 1946 |
MCOILWTICO |
Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma | Dollars per Barrel | Monthly | 1986 |
APU00007471A |
Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in U.S. City Average | U.S. Dollars | Monthly | 1978 |
MHHNGSP |
Henry Hub Natural Gas Spot Price | Dollars per Million BTU | Monthly | 1997 |
DHOILNYH |
No. 2 Heating Oil Prices: New York Harbor | Dollars per Gallon | Daily | 1986 |
WCOILWTICO |
Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma | Dollars per Barrel | Weekly, Ending Friday | 1986 |
APU000072511 |
Average Price: Fuel Oil #2 per Gallon (3.785 Liters) in U.S. City Average | U.S. Dollars | Monthly | 1978 |
IEAXGG |
Exports of Goods: Nonmonetary gold | Millions of Dollars | Quarterly | 1999 |
DGASUSGULF |
Conventional Gasoline Prices: U.S. Gulf Coast, Regular | Dollars per Gallon | Daily | 1986 |
FRDGBSAM |
Federal Reserve Bank Held Gold Bullion: On Display | Fine Troy Ounces | Monthly | 2012 |
G20XTIMVA01CXMLQ |
International Merchandise Trade Statistics: Imports: Commodities for G20 | US dollars, exchange rate converted | Quarterly | 2005 |
WHHNGSP |
Henry Hub Natural Gas Spot Price | Dollars per Million BTU | Weekly | 1997 |
APUS12A74714 |
Average Price: Gasoline, Unleaded Regular (Cost per Gallon/3.785 Liters) in New York-Newark-Jersey City, NY-NJ-PA (CBSA) | U.S. Dollars | Monthly | 1978 |
APUS48A74714 |
Average Price: Gasoline, Unleaded Regular (Cost per Gallon/3.785 Liters) in Phoenix-Mesa-Scottsdale, AZ (CBSA) | U.S. Dollars | Monthly | 2018 |
DGASNYH |
Conventional Gasoline Prices: New York Harbor, Regular | Dollars per Gallon | Daily | 1986 |
APU0000702212 |
Average Price: Bread, Whole Wheat, Pan (Cost per Pound/453.6 Grams) in U.S. City Average | U.S. Dollars | Monthly | 1980 |
MNGLCP |
U.S. Natural Gas Liquid Composite Price | Dollars per Million BTU | Monthly | 2009 |
APUS23B74716 |
Average Price: Gasoline, Unleaded Premium (Cost per Gallon/3.785 Liters) in Detroit-Warren-Dearborn, MI (CBSA) | U.S. Dollars | Monthly | 1981 |
G20XTIMVA01CXMLSAQ |
International Merchandise Trade Statistics: Imports: Commodities for G20 | US dollars, exchange rate converted | Quarterly | 1999 |
IPG211S |
Industrial Production: Mining: Oil and Gas Extraction (NAICS = 211) | Index 2017=100 | Monthly | 1972 |
All 59 series: series.csv. Browse them at https://finzdata.com/macro/category/commodities.
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.
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