series_id large_stringclasses 120
values | date date32 | value float64 -23.6 2.4M |
|---|---|---|
A31SNO | 2025-11-01 | 27,046 |
A31SNO | 2025-12-01 | 27,677 |
A31SNO | 2026-01-01 | 28,004 |
A31SNO | 2026-02-01 | 28,694 |
A31SNO | 2026-03-01 | 28,927 |
A31SNO | 2026-04-01 | 29,705 |
A31SNO | 2026-05-01 | 30,970 |
A31SNO | 2026-06-01 | 31,548 |
A31SNO | 2026-07-01 | 32,222 |
A31SNO | 2026-08-01 | 32,604 |
A32SNO | 2025-11-01 | 42,213 |
A32SNO | 2025-12-01 | 42,574 |
A32SNO | 2026-01-01 | 42,565 |
A32SNO | 2026-02-01 | 43,030 |
A32SNO | 2026-03-01 | 43,025 |
A32SNO | 2026-04-01 | 44,822 |
A32SNO | 2026-05-01 | 45,464 |
A32SNO | 2026-06-01 | 45,420 |
A32SNO | 2026-07-01 | 45,609 |
A32SNO | 2026-08-01 | 44,985 |
A33ATI | 2025-11-01 | 5,700 |
A33ATI | 2025-12-01 | 5,698 |
A33ATI | 2026-01-01 | 5,652 |
A33ATI | 2026-02-01 | 5,676 |
A33ATI | 2026-03-01 | 5,644 |
A33ATI | 2026-04-01 | 5,588 |
A33ATI | 2026-05-01 | 5,555 |
A33ATI | 2026-06-01 | 5,589 |
A33ATI | 2026-07-01 | 5,589 |
A33ATI | 2026-08-01 | 5,696 |
A33ENO | 2025-11-01 | 4,124 |
A33ENO | 2025-12-01 | 4,244 |
A33ENO | 2026-01-01 | 4,218 |
A33ENO | 2026-02-01 | 4,029 |
A33ENO | 2026-03-01 | 4,364 |
A33ENO | 2026-04-01 | 4,713 |
A33ENO | 2026-05-01 | 4,575 |
A33ENO | 2026-06-01 | 5,091 |
A33ENO | 2026-07-01 | 5,596 |
A33ENO | 2026-08-01 | 5,174 |
A33SNO | 2025-11-01 | 40,170 |
A33SNO | 2025-12-01 | 40,255 |
A33SNO | 2026-01-01 | 40,501 |
A33SNO | 2026-02-01 | 41,453 |
A33SNO | 2026-03-01 | 41,963 |
A33SNO | 2026-04-01 | 42,516 |
A33SNO | 2026-05-01 | 43,740 |
A33SNO | 2026-06-01 | 44,307 |
A33SNO | 2026-07-01 | 44,980 |
A33SNO | 2026-08-01 | 45,481 |
A34HNO | 2025-11-01 | 5,288 |
A34HNO | 2025-12-01 | 5,293 |
A34HNO | 2026-01-01 | 5,382 |
A34HNO | 2026-02-01 | 5,348 |
A34HNO | 2026-03-01 | 5,406 |
A34HNO | 2026-04-01 | 5,405 |
A34HNO | 2026-05-01 | 5,579 |
A34HNO | 2026-06-01 | 5,668 |
A34HNO | 2026-07-01 | 5,711 |
A34HNO | 2026-08-01 | 5,769 |
A34SNO | 2025-11-01 | 27,015 |
A34SNO | 2025-12-01 | 27,945 |
A34SNO | 2026-01-01 | 28,163 |
A34SNO | 2026-02-01 | 28,559 |
A34SNO | 2026-03-01 | 29,926 |
A34SNO | 2026-04-01 | 29,813 |
A34SNO | 2026-05-01 | 30,134 |
A34SNO | 2026-06-01 | 31,073 |
A34SNO | 2026-07-01 | 30,873 |
A34SNO | 2026-08-01 | 30,868 |
A34SVS | 2025-11-01 | 32,021 |
A34SVS | 2025-12-01 | 32,273 |
A34SVS | 2026-01-01 | 32,799 |
A34SVS | 2026-02-01 | 33,126 |
A34SVS | 2026-03-01 | 33,317 |
A34SVS | 2026-04-01 | 33,525 |
A34SVS | 2026-05-01 | 33,853 |
A34SVS | 2026-06-01 | 34,691 |
A34SVS | 2026-07-01 | 34,230 |
A34SVS | 2026-08-01 | 34,233 |
A35BNO | 2025-11-01 | 2,321 |
A35BNO | 2025-12-01 | 2,365 |
A35BNO | 2026-01-01 | 2,373 |
A35BNO | 2026-02-01 | 2,282 |
A35BNO | 2026-03-01 | 2,154 |
A35BNO | 2026-04-01 | 2,234 |
A35BNO | 2026-05-01 | 2,267 |
A35BNO | 2026-06-01 | 2,312 |
A35BNO | 2026-07-01 | 2,323 |
A35BNO | 2026-08-01 | 2,251 |
A35SNO | 2025-11-01 | 18,172 |
A35SNO | 2025-12-01 | 18,520 |
A35SNO | 2026-01-01 | 18,349 |
A35SNO | 2026-02-01 | 18,172 |
A35SNO | 2026-03-01 | 18,403 |
A35SNO | 2026-04-01 | 18,435 |
A35SNO | 2026-05-01 | 18,505 |
A35SNO | 2026-06-01 | 18,827 |
A35SNO | 2026-07-01 | 18,822 |
A35SNO | 2026-08-01 | 19,020 |
US macro data: Production (120 series)
120 production series from US government sources, cleaned into one consistent format and ready for pandas. This free package holds the last 12 months (2025-10-26 on) of each series' latest values. It is refreshed weekly.
import pandas as pd
df = pd.read_parquet("hf://datasets/FinzData/macro-production/data/long.parquet") # series_id, date, value
wide = pd.read_parquet("hf://datasets/FinzData/macro-production/data/wide_monthly.parquet") # one column per series
or datasets.load_dataset("FinzData/macro-production"). Wide tables by frequency: wide_monthly (105 series), wide_quarterly (15 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("INDPRO") # full history, latest values
c.macro("INDPRO", vintage="first") # as first released
c.macro("INDPRO", as_of="2020-03-01") # exactly what was known on that date
c.macro_series(category="production") # the catalogue for this category
Most-watched series in this package
| ID | Title | Units | Frequency | Full history from |
|---|---|---|---|---|
INDPRO |
Industrial Production: Total Index | Index 2017=100 | Monthly | 1919 |
IPMAN |
Industrial Production: Manufacturing (NAICS) | Index 2017=100 | Monthly | 1972 |
TCU |
Capacity Utilization: Total Index | Percent | Monthly | 1967 |
DGORDER |
Manufacturers' New Orders: Durable Goods | Millions of Dollars | Monthly | 1958 |
DAUPSA |
Domestic Auto Production | Thousands of Units | Monthly | 1993 |
MCUMFN |
Capacity Utilization: Manufacturing (NAICS) | Percent | Monthly | 1972 |
OUTMS |
Manufacturing Sector: Real Sectoral Output for All Workers | Index 2017=100 | Quarterly | 1949 |
C307RX1Q020SBEA |
Real private fixed investment: Nonresidential: Structures: Manufacturing | Billions of Chained 2017 Dollars | Quarterly | 1995 |
MNFCTRIRSA |
Manufacturers: Inventories to Sales Ratio | Ratio | Monthly, End of Period | 1992 |
IPG3344S |
Industrial Production: Manufacturing: Durable Goods: Semiconductor and Other Electronic Component (NAICS = 3344) | Index 2017=100 | Monthly | 1972 |
A34SNO |
Manufacturers' New Orders: Computers and Electronic Products | Millions of Dollars | Monthly | 1992 |
IPG3361T3S |
Industrial Production: Manufacturing: Durable Goods: Motor Vehicles and Parts (NAICS = 3361-3) | Index 2017=100 | Monthly | 1972 |
IPMANSICS |
Industrial Production: Manufacturing (SIC) | Index 2017=100 | Monthly | 1919 |
GOMA |
Gross Output by Industry: Manufacturing | Billions of Dollars | Quarterly | 2005 |
CAPUTLG3311A2S |
Capacity Utilization: Manufacturing: Durable Goods: Iron and Steel Products (NAICS = 3311,2) | Percent | Monthly | 1972 |
ADXTNO |
Manufacturers' New Orders: Durable Goods Excluding Transportation | Millions of Dollars | Monthly | 1992 |
BSCICP02USM460S |
Business Tendency Surveys (Manufacturing): Confidence Indicators: Composite Indicators: National Indicator for United States | Percent | Monthly | 1950 |
C307RC1Q027SBEA |
Private fixed investment: Nonresidential: Structures: Manufacturing | Billions of Dollars | Quarterly | 1958 |
IPG325S |
Industrial Production: Manufacturing: Nondurable Goods: Chemical (NAICS = 325) | Index 2017=100 | Monthly | 1972 |
ACOGNO |
Manufacturers' New Orders: Consumer Goods | Millions of Dollars | Monthly | 1992 |
IPB50001N |
Industrial Production: Total Index | Index 2017=100 | Monthly | 1919 |
IPN3311A2RS |
Industrial Production: Manufacturing: Durable Goods: Raw Steel (NAICS = 3311,2pt.) | Index 2017=100 | Monthly | 1972 |
RVAMA |
Real Value Added by Industry: Manufacturing | Billions of Chained 2017 Dollars | Quarterly | 2005 |
IPG334S |
Industrial Production: Manufacturing: Durable Goods: Computer and Electronic Product (NAICS = 334) | Index 2017=100 | Monthly | 1972 |
IPUTIL |
Industrial Production: Utilities: Electric and Gas Utilities (NAICS = 2211,2) | Index 2017=100 | Monthly | 1939 |
All 120 series: series.csv. Browse them at https://finzdata.com/macro/category/production.
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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